Fish and health with national fish promotion programme
This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.
Knit to html for best performance.
Calculation is based on BONUS GOHERR project and its fish health benefit-risk assessment.
What needs to be done for PFAS assessment?
- Amount should be gender and age-specific, because we need to worry about young mothers. Solution: take KKE amounts of fish, and scale those with the Goherr subgroup-specific proportions. DONE. Not age-specific atm.
- Amounts should reflect the actual fish consumption in Porvoo. Solution: postpone and use national statistics. DONE
- Infant’s dioxin concentration module must be added. Solution: Use Goherr model for expo_indir DONE
- The module must be updated to match PFAS as well. Solution: update the module to contain column Exposure_agent. Change body fat parameter to volume of distribution. DONE but not checked.
- Find why expo_indir is so much higher than EFSA toxicokinetic assessment. Make an alternative model.
- Add ERF for PFAS (sum of PFOS, PFHxS, PFOA, PFNA). Solution: Make a new page for ERF of PFAS and add that to adjusted ERFs. First immunology; postpone cholesterol and low birth weight and liver toxicity. DONE
- Add case burdens for PFAS outcomes. Solution: give rough estimates for immunology to get started. DONE
- Add PFAS concentrations to data. Solution: Look at Porvoo concentrations first and make conc_pfas; combine that with conc_vit. DONE
- Add dioxin and MeHg concentrations. DONE
- Now that the model runs technically, look through each part to check that it makes sense.
- If PAF contains some inputs with Age and some without Age (implying that they apply to any age group), only the explicit age groups remain in the end. Solution: Add RRorig to compensate for this. DONE
- TEQ and MeHg are missing from dose. Check why and correct. DONE
- The proportions of All-cause mortality and CHD2 mortality are 3:1 in Kotimaisen kalan edistämisohjelma but 3:2 in this analysis. Why?
- In exposure, ALA + DHA < Omega3. Why?
- BoDattr: age group 45 - 50 is way smaller than any other group. Why? Solution: it’s the misplaces 5-9-year age group. DONE
- conc_mehg does not contain Iter although it is probabilistic. Solution: Update formula. DONE
- TEQ response for cancer morbidity is way too large. Why? Is there a unit problem? Let’s look at age group 45-49. BoD 3800, BoDattr 970, dose (Female) 188, CSF 0.0005, incidence 0.00657, population (Female) 154000. This makes case burden = BoD / I / P = 3800/0.00657/154000 = 3.7 DALY/case which is ten times higher than Goherr case burden 0.39 DALY/case. The reason is that the former is about life-time exposure while the latter is per one-year exposure, which is the correct way. The current PAF implies that 21 and 31 % of cancers in females and males, respectively are caused by TEQ in all age groups. That’s an artifact also from the lifetime vs yearly mismatch.
- MeHg exposure is ca. 2 ug/d when you would assume 1 mg/kg * 0.03 kg/d = 30 ug/d. Where is the difference?
- Sperm concentration and dental defect show fairly large BoD. This is because the ERFs have exposure units pg/g, while the exposure has pg/d. Solution: implement the expo_dir submodule.
- Why is average fish so much higher with PFAS concentration than other fish species and the expectation? Because individual samples are accidentally summed, not bootstrapped. Solution: update formula for conc_pfas.
objects.latest("Op_en2261", code_name="RR2") # RR on page [[Health impact assessment]]## Loading objects:
## RRRRorig <- RR@formula
RR@formula <- function(...) {
out <- RRorig()
out <- out * Ovariable(
output=data.frame(Age = ages, Result=1),
marginal=c(TRUE,FALSE)
)
return(out)
}# This code was forked from https://github.com/jtuomist/fishhealth/blob/master/fishhealth.Rmd
# This code was previously forked from code Op_fi5923/model on page [[Kotimaisen kalan edistämisohjelma]]
# The code was even more previously forked from Op_fi5889/model on page [[Ruori]] and Op_en7748/model on page [[Goherr assessment]]
dat <- opbase.data("Op_fi5932", subset="Malliparametrit")[-1] # [[PFAS-yhdeisteiden tautitaakka]]
dec <- opbase.data("Op_fi5932", subset="Decisions")[-1]
DecisionTableParser(dec)
CTable <- opbase.data("Op_fi5932",subset="CollapseMarginals")
#for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} # The default is currently character, not factor
CollapseTableParser(CTable)
cat("Laskennassa käytetty data.\n")## Laskennassa käytetty data.datcat("Tarkastellut päätökset.\n")## Tarkastellut päätökset.deccat("Aggregoidut marginaalit.\n")## Aggregoidut marginaalit.CTabledummy <- Ovariable("dummy",data=data.frame(Age="dummy", Fish="dummy", Compound="dummy", Area="dummy", Result=1)) # Keep these columns marginals
fish_proportion <- Ovariable( # How population subgroups eat fish differently
"fish_proportion",
data = data.frame(prepare(dat,"fish proportion",c("Type","Exposure_agent","Response","Unit"))),
unit="proportion of the mean")
total_amount <- Ovariable(
"total_amount",
data=prepare(dat, "amount", c("Type","Response","Exposure_agent","Unit")),
unit="M kg/a")
amount <- Ovariable(
"amount",
dependencies = data.frame(Name=c("total_amount", "fish_proportion")),
formula = function(...) {
amount <- total_amount
# Filleted weight, i.e. no loss.
amount <- amount * 1000 / 5.52 /365.25
# M kg/a per 5.52M population --> g/d per average person.
amount <- amount * fish_proportion
# fish_proportion tells the relative amount in each subgroup
# Match KKE-classification in amount with Fineli classification
tmp <- Ovariable(
output = data.frame(
Kala = c("Kasvatettu", "Kaupallinen", "Kirjolohi", "Silakka", "Vapaa-ajan", "Muu tuonti", "Tuontikirjolohi", "Tuontilohi"),
Fish = c("Whitefish", "Average fish","Rainbow trout", "Herring", "Average fish", "Average fish", "Rainbow trout", "Salmon"),
Result = 1
),
marginal = c(TRUE, TRUE, FALSE)
)
amount <- amount * tmp
return(amount)
},
unit="g/d"
)
# Exposure:To child and To eater not needed, because dioxins are not (yet) included
population <- Ovariable(
"population",
data=prepare(dat, "population", c("Type", "Exposure_agent", "Response","Unit")),
unit = "#"
)
population@data$Age <- factor(population@data$Age, levels=levels(ages))
incidence <- Ovariable(
"incidence",
data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit")),
unit="1/person-year")
#incidence@data$Age[is.na(incidence@data$Age)] <- ""
case_burden <- Ovariable(
"case_burden",
data = prepare(dat,"case burden",c("Type", "Exposure_agent","Unit")),
unit="DALY/case")
ERFchoice <- Ovariable(
"ERFchoice",
data =
prepare(dat, "ERFchoice", c("Unit", "Type"))
)
InpBoD <- EvalOutput(Ovariable( # Evaluated because is not a dependency but an Input
"InpBoD",
data = prepare(dat, "BoD", c("Type","Exposure_agent","Unit")),
unit="DALY/a"
))
InpBoD$Response[InpBoD$Response=="All causes"] <- "All-cause mortality"
InpBoD$Response[InpBoD$Response=="Depressive disorders"] <- "Depression"
InpBoD$Response[InpBoD$Response=="Neoplasms"] <- "Cancer morbidity yearly"
InpBoD$Response[InpBoD$Response=="Respiratory infections and tuberculosis"] <- "Immunosuppression" # Infections of 0-9-year-olds are assumed to represent the background BoD of immunosuppressive effect of PFAS
InpBoD$Response[InpBoD$Response=="Cardiovascular diseases"] <- "CHD2 mortality"conc_vit <- Ovariable(
"conc_vit",
ddata = "Op_en1838", # [[Concentrations of beneficial nutrients in fish]]
subset = "Fineli data for common fish species",
unit="ALA mg/g, DHA mg/g, Fish g/g, Omega3 mg/g, Vitamin D µg/g f.w. after adjustment"
)
df = conc_vit@data
df$Nutrient[df$Nutrient=="D-vitamiini (µg)"] <- "Vitamin D"
df$Nutrient[df$Nutrient=="rasvahapot n-3 moni-tyydyttymättömät (g)"] <- "Omega3"
df$Nutrient[df$Nutrient=="rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)"] <- "ALA"
df$Nutrient[df$Nutrient=="rasvahappo 22:6 n-3 (DHA) (mg)"] <- "DHA"
df$Nutrient[df$Nutrient=="proteiini (g)"] <- "Fish"
df$conc_vitResult[df$Nutrient=="Fish"] <- "1"
df <- dropall(df[df$Nutrient %in% c("Vitamin D", "Omega3", "ALA", "DHA", "Fish") , ])
conc_vit@data <- df
######## Concentration of PFAS
# Data from EU-kalat3 (Finland excl Vanhankaupunginlahti): # pg/g fresh weight
# POP mean sd min Q0.025 median Q0.975 max
# 2.5% PFOS 2055.757 1404.045 305.0399 330.1365 1533.269 5029.697 5814.935
# Data from EU-kalat3 (Vanhankaupunginlahti, Helsinki) # ng/g f.w.
# POP mean sd min Q0.025 median Q0.975 max
#2.5% PFOS 14.428 11.94542 1.499441 1.607789 15.64988 35.32517 38.91994
conc_eukalat <- EvalOutput(Ovariable(
"conc_eukalat",
data = data.frame(
Area = c("Suomi","Helsinki"),
Compound="PFOS",
Result=c("2.056 (3.301 - 5.030)", "14.428 (1.499 - 35.325)")),
unit="ng/g fresh weight"
))
conc_pfas_raw <- Ovariable(
"conc_pfas_raw",
ddata="Op_fi5932",subset="PFAS concentrations",
unit="ng/g f.w.")
conc_pfas <- Ovariable(
"conc_pfas",
dependencies = data.frame(Name="conc_pfas_raw"),
formula = function(...) {
out <- conc_pfas_raw
out$Source <- NULL
out$Area <- "Porvoo"
out <- expand_index(out, list(Fish=list(Perch=c(
"Average fish", "Pike","Rainbow trout","Roach", "Salmon", "Vendace", "Whitefish"))))
out <- out[sample(nrow(out@output)),]
out <- out[!duplicated(out@output[intersect(colnames(out@output),c("Fish","Compound","Iter"))]),]
out@marginal <- c(TRUE, TRUE, FALSE, TRUE, TRUE)
return(out)
},
unit="ng/g fresh weight"
)
#sum_pfas <- oapply(conc_pfas, cols=c("Kala","Compound"), FUN=sum)
#tmp <- conc_pfas / sum_pfas
#summary(tmp, marginals="Compound")
#
## This tells that PFOS consists of 71 - 97 % of the four key PFAS, while PFOA, PFNA, and PFHxS consist of
# 0 - 10 %, 2 - 18 %, and 0 - 9 %, respectively.
# Even if we included the next most abundant congeners, i.e. PFDA and PFUnA, the overall picture would not change.
conc <- Ovariable(
"conc",
dependencies = data.frame(
Name=c("conc_vit", "conc_pfas","conc_pcddf","conc_mehg"),
Ident = c(NA,NA,"Op_en3104/pcddf_mean","Op_en4004/conc_mehg_allfish")),
formula = function(...){
conc_vit <- oapply(conc_vit, cols=c("Kala", "Adjust"),FUN=mean)
colnames(conc_vit@output)[colnames(conc_vit@output)=="Nutrient"] <- "Compound"
# conc_pfas <- oapply(conc_pfas, cols=c("Obs","Area"), FUN=mean) # Redundant because handled already
conc_pfas$Compound[conc_pfas$Compound %in% c("PFOA","PFNA","PFHxS","PFOS")] <- "PFAS"
conc_pfas <- oapply(conc_pfas, cols="Area", FUN=sum) # Works because only one area
conc_pcddf$Compound <- "TEQ"
conc_mehg <- oapply(conc_mehg[conc_mehg$Area=="Porvoo",], NULL, mean, c("Area","Kala")) # UPDATE FOR PROBABILISTIC APPROACH
conc_mehg$Compound <- "MeHg"
out <- OpasnetUtils::combine(conc_vit, conc_pfas, conc_pcddf, conc_mehg)
return(out)
}
)
#conc <- EvalOutput(conc, verbose=TRUE)
#View(conc@output)
#View(conc_pcddf@output)
#View(conc_mehg@output)###################################################################
# Code copied from http://en.opasnet.org/w/Goherr_assessment#
mc2dparam<- list(
N2 = 10, # Number of iterations in the new Iter
strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations
run2d = FALSE, # Should the mc2d function be used or not?
info = 1, # Ovariable that contains additional indices, e.g. newmarginals.
newmarginals = c("Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals
method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option.
fun = mean # Function for aggregating the first Iter dimension.
)
if(FALSE) {
## Exposure with background exposure but without mother's exposure to child
expo_dir <- Ovariable(
"expo_dir",
dependencies=data.frame(Name=c("amount","conc","expo_bg")),
formula = function(...) {
out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1
out$Exposure_agent <- "Fish"
out <- combine(conc, out, name="conc")
out <- oapply(amount * out, cols="Fish", FUN=sum)
out <- Ovariable(output = data.frame(
Exposcen = c("BAU", "No exposure"),
Result = c(1, 0)
), marginal=c(TRUE,FALSE)) * out + expo_bg
out$Exposure <- as.factor(
ifelse(
out$Exposure_agent %in% c("DHA", "MeHg"),
"To child",
"To eater"
)
)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d"
)
## Background-exposure to vitamin D and omega-3
addexposure <- Ovariable(
"addexposure",
ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]]
subset = "Background exposure",
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Should the background be specific for gender and country? At the moment it is.
expo_bg <- Ovariable(
"expo_bg",
dependencies = data.frame(Name="addexposure","info"),
formula = function(...) {
out <- addexposure
# Empty values ("") in indices must be replaced by NA so that Ops works correctly.
levels(out$Gender)[levels(out$Gender) == ""] <- NA
levels(out$Country)[levels(out$Country) == ""] <- NA
levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA
out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent"))
temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ]
temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum)
temp1$Exposure_agent <- "TEQ"
temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ]
temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum)
temp2$Exposure_agent <- "Omega3"
out <- OpasnetUtils::combine(out, temp1, temp2)
out <- unkeep(out * info, prevresults = TRUE, sources = TRUE)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Stores non-marginal columns for further use.
info <- Ovariable(
"info",
dependencies = data.frame(Name = c("jsp")),
formula = function(...) {
out <- jsp
out$Group <- factor(
paste(out$Gender, out$Ages),
levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45")
)
out$Country <- factor(out$Country, ordered=FALSE)
out <- unique(out@output[c("Iter","Country","Group","Gender","Row")])
out$Result <- 1
return(out)
}
)
} # END IF
############################### Code from Goherr assessment ends
exposure <- Ovariable(
"exposure",
dependencies = data.frame(
Name = c(
"expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself
"expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother)
"mc2d" # 2D Monte Carlo function
),
Ident = c(
NA,
"Op_en7797/expo_indir2", # [[Infant's dioxin exposure]] # expo_indir
"Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]]
),
formula = function(...) {
# expo_indir$Exposure <- NULL
expo_indir <- expo_indir[expo_indir$Gender =="Female",]
expo_indir$Gender <- NULL
expo_dir$Exposure <- "Direct"
out <- OpasnetUtils::combine(expo_dir, expo_indir)
out <- unkeep(out, "Source.1", sources=TRUE)
out <- mc2d(out)
return(out)
},
unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)
expo_dir <- Ovariable(
"expo_dir",
dependencies=data.frame(Name=c("amount","conc")),
formula = function(...) {
out <- amount * conc
colnames(out@output)[colnames(out@output)=="Compound"] <- "Exposure_agent"
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /day; Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)objects.latest("Op_en2261",code_name="BoDattr2") # [[Health impact assessment]]## Loading objects:
## BoDattrtryCatch(BoDattr <- EvalOutput(BoDattr, verbose=TRUE))## Evaluating BoDattr ...
##
## - BoD fetched successfully!
##
## - PAF fetched successfully!
## - Evaluating BoD ...
## - - Evaluating incidence ...
##
## done(0.01 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).## Loading required package: reshape2##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smiths## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
##
## done(0 secs)!
## - - Checking case_burden marginals ... Response, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ... done!
## - - Evaluating population ...
##
## done(0 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
##
## - done(0.3 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.## done!
## - Evaluating PAF ...
##
## - - dose fetched successfully!
##
## - - ERF fetched successfully!
##
## - - frexposed fetched successfully!
##
## - - P_illness fetched successfully!
##
## - - sumExposcen fetched successfully!
##
## - - mc2d fetched successfully!
## - - Evaluating dose ...
##
## - - - BW fetched successfully!
## - - - Evaluating exposure ...
##
## - - - - expo_indir fetched successfully!
## - - - - Evaluating expo_dir ...
## - - - - - Evaluating amount ...
## - - - - - - Evaluating total_amount ...
##
## done(0 secs)!
## - - - - - - Checking total_amount marginals ... Kala, Scenario, total_amountSource recognized as marginal(s).
## - - - - - - Evaluating fish_proportion ...
##
## done(0 secs)!
## - - - - - - Checking fish_proportion marginals ... Gender, fish_proportionSource recognized as marginal(s).
##
## ----- done(0.16 secs)!
## - - - - - Checking amount marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource recognized as marginal(s).
## - - - - - Evaluating conc ...
##
## - - - - - - conc_pcddf fetched successfully!
##
## - - - - - - conc_mehg fetched successfully!
## - - - - - - Evaluating conc_vit ...
##
## done(0.02 secs)!
## - - - - - - Checking conc_vit marginals ... Kala, Fish, Nutrient, conc_vitSource recognized as marginal(s).
## - - - - - - Processing conc_vit decisions ... done!
## - - - - - - Evaluating conc_pfas ...
## - - - - - - - Evaluating conc_pfas_raw ...
##
## done(0.03 secs)!
## - - - - - - - Checking conc_pfas_raw marginals ... Fish, Compound, conc_pfas_rawSource recognized as marginal(s).
##
## ------ done(0.11 secs)!
## - - - - - - Checking conc_pfas marginals ... Fish, Compound, conc_pfas_rawSource, Area, conc_pfasSource recognized as marginal(s).
## - - - - - - Processing conc_pfas marginal collapses ... done!
## - - - - - - Evaluating conc_pcddf ...
##
## - - - - - - - TEF fetched successfully!
## - - - - - - - Evaluating TEF ...
##
## - - - - - - - - TEFversion fetched successfully!
## - - - - - - - - Evaluating TEFraw ...
##
## done(0.01 secs)!
## - - - - - - - - Checking TEFraw marginals ... Group, Compound, TEFversion, TEFrawSource recognized as marginal(s).
##
## ------- done(24.89 secs)!
## - - - - - - - Checking TEF marginals ... Compound, Group, TEFversion, TEFrawSource, TEFSource recognized as marginal(s).
##
## ------ done(1.04 mins)!
## - - - - - - Checking conc_pcddf marginals ... Fish, conc_pcddfSource recognized as marginal(s).
## - - - - - - Evaluating conc_mehg ...
##
## - - - - - - - Hg fetched successfully!
##
## ------ done(24.05 secs)!
## - - - - - - Checking conc_mehg marginals ... Area, Kala, Fish, conc_mehgSource recognized as marginal(s).
##
## ----- done(2.28 mins)!
## - - - - - Checking conc marginals ... Fish, Compound, concSource recognized as marginal(s).
##
## ---- done(2.28 mins)!
## - - - - Checking expo_dir marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource, Exposure_agent, concSource, expo_dirSource recognized as marginal(s).
## - - - - Evaluating expo_indir ...
## - - - - - Evaluating t0.5 ...
##
## done(0 secs)!
## - - - - - Checking t0.5 marginals ... Exposure_agent, t0.5Source recognized as marginal(s).
## - - - - - Evaluating f_ing ...
##
## done(0 secs)!
## - - - - - Checking f_ing marginals ... Exposure_agent, f_ingSource recognized as marginal(s).
## - - - - - Evaluating f_mtoc ...
##
## done(0 secs)!
## - - - - - Checking f_mtoc marginals ... Exposure_agent, f_mtocSource recognized as marginal(s).
## - - - - - Evaluating BF ...
##
## done(0 secs)!
## - - - - - Checking BF marginals ... Exposure_agent, BFSource recognized as marginal(s).
##
## ---- done(0.24 secs)!
## - - - - Checking expo_indir marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource, Exposure_agent, concSource, expo_dirSource, f_ingSource, t0.5Source, f_mtocSource, BFSource, Exposure, expo_indirSource recognized as marginal(s).
## - - - - Processing expo_indir marginal collapses ... done!
##
## --- done(2.68 mins)!
## - - - Checking exposure marginals ... Kala, Scenario, Gender, Fish, Exposure_agent, Exposure, exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying exposure, found NAs in indices: Gender. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.## done!
## - - - Evaluating BW ...
##
## done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
##
## -- done(3.09 mins)!
## - - Checking dose marginals ... Scenario, Gender, Exposure_agent, Exposure, Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
##
## - - - ERF_env fetched successfully!
##
## - - - ERF_omega3 fetched successfully!
##
## - - - ERF_mehg fetched successfully!
##
## - - - ERF_diox fetched successfully!
##
## - - - ERF_vit fetched successfully!
##
## - - - ERF_micr fetched successfully!
##
## - - - ERF_pfas fetched successfully!
## - - - Evaluating ERF_env ...
##
## done(0.02 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
##
## done(0 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
##
## done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
##
## done(0 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
##
## done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
##
## done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
##
## done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
##
## done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
##
## -- done(2.77 mins)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
##
## -- done(0.17 secs)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, Scenario, Gender, Exposure, doseSource, Age, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
##
## done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
##
## done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
##
## - done(8.38 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, Scenario, Gender, Exposure, doseSource, frexposedSource, Age, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Age, Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.## done!
##
## done(9.23 mins)!
## Checking BoDattr marginals ... Response, Age, Gender, Adjust, InpBoDSource, Exposure_agent, Scenario, PAFSource, BoDattrSource recognized as marginal(s).oprint(summary(amount,"mean"))## Kala Scenario Gender Fish mean
## 1 Kaupallinen BAU Female Average fish 1.2697279
## 2 Muu tuonti BAU Female Average fish 11.1101191
## 3 Vapaa-ajan BAU Female Average fish 3.9282207
## 4 Kaupallinen BAU Male Average fish 1.9045919
## 5 Muu tuonti BAU Male Average fish 16.6651787
## 6 Vapaa-ajan BAU Male Average fish 5.8923310
## 7 Silakka BAU Female Herring 0.6348640
## 8 Silakka BAU Male Herring 0.9522959
## 9 Kirjolohi BAU Female Rainbow trout 2.6584928
## 10 Tuontikirjolohi BAU Female Rainbow trout 1.9045919
## 11 Kirjolohi BAU Male Rainbow trout 3.9877392
## 12 Tuontikirjolohi BAU Male Rainbow trout 2.8568878
## 13 Tuontilohi BAU Female Salmon 9.3642433
## 14 Tuontilohi BAU Male Salmon 14.0463649
## 15 Kasvatettu BAU Female Whitefish 0.2380740
## 16 Kasvatettu BAU Male Whitefish 0.3571110oprint(summary(BoD,marginals=c("Age","Response"),"mean"))## Age Response mean
## 1 0 - 4 Cancer morbidity yearly 309.5200
## 2 10 - 14 Cancer morbidity yearly 347.0750
## 3 15 - 19 Cancer morbidity yearly 414.9750
## 4 20 - 24 Cancer morbidity yearly 569.5250
## 5 25 - 29 Cancer morbidity yearly 795.4550
## 6 30 - 34 Cancer morbidity yearly 1147.9850
## 7 35 - 39 Cancer morbidity yearly 1679.1250
## 8 40 - 44 Cancer morbidity yearly 2452.5650
## 9 45 - 49 Cancer morbidity yearly 3849.3800
## 10 5 - 9 Cancer morbidity yearly 345.5200
## 11 50 - 54 Cancer morbidity yearly 6987.9000
## 12 55 - 59 Cancer morbidity yearly 11237.3300
## 13 60 - 64 Cancer morbidity yearly 16331.9100
## 14 65 - 69 Cancer morbidity yearly 21899.6500
## 15 70 - 74 Cancer morbidity yearly 25551.8600
## 16 75 - 79 Cancer morbidity yearly 18015.2300
## 17 80 - 84 Cancer morbidity yearly 14650.9150
## 18 85 - 89 Cancer morbidity yearly 8413.0550
## 19 90 - 94 Cancer morbidity yearly 3537.1750
## 20 Undefined Dioxin recommendation tolerable daily intake 305.4067
## 21 Undefined Dioxin recommendation tolerable daily intake 2018 888.4560
## 22 0 - 4 Immunosuppression 295.9850
## 23 5 - 9 Immunosuppression 261.5350
## 24 0 - 4 Loss in child's IQ points 16778.3774
## 25 Undefined PFAS TWI 2776.4249
## 26 0 - 4 Sperm concentration 4478.6700
## 27 Undefined Vitamin D recommendation 610.8135
## 28 0 - 4 Yes or no dental defect 343.9619
## 29 0 - 4 All-cause mortality 4867.4650
## 30 10 - 14 All-cause mortality 1085.5450
## 31 15 - 19 All-cause mortality 2965.9450
## 32 20 - 24 All-cause mortality 5274.8450
## 33 25 - 29 All-cause mortality 6263.5400
## 34 30 - 34 All-cause mortality 6710.3100
## 35 35 - 39 All-cause mortality 8275.6450
## 36 40 - 44 All-cause mortality 10232.0150
## 37 45 - 49 All-cause mortality 13691.2800
## 38 5 - 9 All-cause mortality 931.1250
## 39 50 - 54 All-cause mortality 21321.8650
## 40 55 - 59 All-cause mortality 29861.9100
## 41 60 - 64 All-cause mortality 39200.8750
## 42 65 - 69 All-cause mortality 49295.5050
## 43 70 - 74 All-cause mortality 59799.7000
## 44 75 - 79 All-cause mortality 49428.5150
## 45 80 - 84 All-cause mortality 51968.9150
## 46 85 - 89 All-cause mortality 42765.3450
## 47 90 - 94 All-cause mortality 26756.6400
## 48 0 - 4 Depression 0.4150
## 49 10 - 14 Depression 555.8850
## 50 15 - 19 Depression 1320.8250
## 51 20 - 24 Depression 1841.0150
## 52 25 - 29 Depression 1801.4650
## 53 30 - 34 Depression 1590.0200
## 54 35 - 39 Depression 1721.7250
## 55 40 - 44 Depression 1700.2800
## 56 45 - 49 Depression 1534.1700
## 57 5 - 9 Depression 76.4850
## 58 50 - 54 Depression 1718.9550
## 59 55 - 59 Depression 1767.1650
## 60 60 - 64 Depression 1676.7400
## 61 65 - 69 Depression 1561.5750
## 62 70 - 74 Depression 1426.2350
## 63 75 - 79 Depression 799.7000
## 64 80 - 84 Depression 581.2950
## 65 85 - 89 Depression 363.0750
## 66 90 - 94 Depression 186.1150
## 67 0 - 4 CHD2 mortality 53.6100
## 68 10 - 14 CHD2 mortality 75.3150
## 69 15 - 19 CHD2 mortality 145.3450
## 70 20 - 24 CHD2 mortality 254.1000
## 71 25 - 29 CHD2 mortality 437.6450
## 72 30 - 34 CHD2 mortality 636.4200
## 73 35 - 39 CHD2 mortality 1168.2600
## 74 40 - 44 CHD2 mortality 1977.5650
## 75 45 - 49 CHD2 mortality 3117.3100
## 76 5 - 9 CHD2 mortality 48.7800
## 77 50 - 54 CHD2 mortality 5476.2900
## 78 55 - 59 CHD2 mortality 8747.4650
## 79 60 - 64 CHD2 mortality 13077.9700
## 80 65 - 69 CHD2 mortality 18100.7500
## 81 70 - 74 CHD2 mortality 24549.2850
## 82 75 - 79 CHD2 mortality 23413.4850
## 83 80 - 84 CHD2 mortality 27899.3350
## 84 85 - 89 CHD2 mortality 25240.0350
## 85 90 - 94 CHD2 mortality 16630.6150
## 86 15 - 19 Breast cancer 2.0900
## 87 20 - 24 Breast cancer 7.7850
## 88 25 - 29 Breast cancer 36.8200
## 89 30 - 34 Breast cancer 145.1600
## 90 35 - 39 Breast cancer 263.6000
## 91 40 - 44 Breast cancer 433.3500
## 92 45 - 49 Breast cancer 704.9550
## 93 50 - 54 Breast cancer 1045.3250
## 94 55 - 59 Breast cancer 1341.8150
## 95 60 - 64 Breast cancer 1513.2150
## 96 65 - 69 Breast cancer 1649.3300
## 97 70 - 74 Breast cancer 1678.0350
## 98 75 - 79 Breast cancer 1232.0100
## 99 80 - 84 Breast cancer 950.8800
## 100 85 - 89 Breast cancer 578.1900
## 101 90 - 94 Breast cancer 296.1800oprint(summary(BoDattr,marginals=c("Age","Response"),"mean"))## Age Response mean
## 1 0 - 4 Cancer morbidity yearly 1.785242e+00
## 2 10 - 14 Cancer morbidity yearly 1.956941e+00
## 3 15 - 19 Cancer morbidity yearly 2.381629e+00
## 4 20 - 24 Cancer morbidity yearly 3.274527e+00
## 5 25 - 29 Cancer morbidity yearly 4.577121e+00
## 6 30 - 34 Cancer morbidity yearly 6.480503e+00
## 7 35 - 39 Cancer morbidity yearly 9.443872e+00
## 8 40 - 44 Cancer morbidity yearly 1.368822e+01
## 9 45 - 49 Cancer morbidity yearly 2.145437e+01
## 10 5 - 9 Cancer morbidity yearly 1.951139e+00
## 11 50 - 54 Cancer morbidity yearly 3.939245e+01
## 12 55 - 59 Cancer morbidity yearly 6.458151e+01
## 13 60 - 64 Cancer morbidity yearly 9.463669e+01
## 14 65 - 69 Cancer morbidity yearly 1.275639e+02
## 15 70 - 74 Cancer morbidity yearly 1.487741e+02
## 16 75 - 79 Cancer morbidity yearly 1.042378e+02
## 17 80 - 84 Cancer morbidity yearly 8.398121e+01
## 18 Undefined Dioxin recommendation tolerable daily intake -2.165611e+03
## 19 Undefined Dioxin recommendation tolerable daily intake 2018 1.776912e+03
## 20 0 - 4 Immunosuppression 7.714128e+00
## 21 5 - 9 Immunosuppression 8.254486e+00
## 22 0 - 4 Loss in child's IQ points 6.974660e+02
## 23 Undefined PFAS TWI 1.370935e+03
## 24 0 - 4 Sperm concentration 5.024148e+03
## 25 Undefined Vitamin D recommendation 6.108135e+02
## 26 0 - 4 Yes or no dental defect 2.795378e+03
## 27 0 - 4 All-cause mortality -3.940215e+02
## 28 10 - 14 All-cause mortality -8.729181e+01
## 29 15 - 19 All-cause mortality -2.510075e+02
## 30 20 - 24 All-cause mortality -4.582433e+02
## 31 25 - 29 All-cause mortality -5.472993e+02
## 32 30 - 34 All-cause mortality -5.805901e+02
## 33 35 - 39 All-cause mortality -7.164739e+02
## 34 40 - 44 All-cause mortality -8.793680e+02
## 35 45 - 49 All-cause mortality -1.166310e+03
## 36 5 - 9 All-cause mortality -7.424681e+01
## 37 50 - 54 All-cause mortality -1.808052e+03
## 38 55 - 59 All-cause mortality -2.535812e+03
## 39 60 - 64 All-cause mortality -3.319267e+03
## 40 65 - 69 All-cause mortality -4.145066e+03
## 41 70 - 74 All-cause mortality -4.980650e+03
## 42 75 - 79 All-cause mortality -4.044850e+03
## 43 80 - 84 All-cause mortality -4.134937e+03
## 44 0 - 4 Depression -7.647127e-02
## 45 10 - 14 Depression -9.937062e+01
## 46 15 - 19 Depression -2.347323e+02
## 47 20 - 24 Depression -3.276647e+02
## 48 25 - 29 Depression -3.226701e+02
## 49 30 - 34 Depression -2.865779e+02
## 50 35 - 39 Depression -3.110681e+02
## 51 40 - 44 Depression -3.073361e+02
## 52 45 - 49 Depression -2.766914e+02
## 53 5 - 9 Depression -1.387565e+01
## 54 50 - 54 Depression -3.090770e+02
## 55 55 - 59 Depression -3.166728e+02
## 56 60 - 64 Depression -2.997941e+02
## 57 65 - 69 Depression -2.791761e+02
## 58 70 - 74 Depression -2.551780e+02
## 59 75 - 79 Depression -1.425020e+02
## 60 80 - 84 Depression -1.024717e+02
## 61 0 - 4 CHD2 mortality -8.359787e+00
## 62 10 - 14 CHD2 mortality -1.170566e+01
## 63 15 - 19 CHD2 mortality -2.262663e+01
## 64 20 - 24 CHD2 mortality -3.966234e+01
## 65 25 - 29 CHD2 mortality -6.844631e+01
## 66 30 - 34 CHD2 mortality -9.964232e+01
## 67 35 - 39 CHD2 mortality -1.833859e+02
## 68 40 - 44 CHD2 mortality -3.106654e+02
## 69 45 - 49 CHD2 mortality -4.899542e+02
## 70 5 - 9 CHD2 mortality -7.579246e+00
## 71 50 - 54 CHD2 mortality -8.613391e+02
## 72 55 - 59 CHD2 mortality -1.375930e+03
## 73 60 - 64 CHD2 mortality -2.055932e+03
## 74 65 - 69 CHD2 mortality -2.841726e+03
## 75 70 - 74 CHD2 mortality -3.847268e+03
## 76 75 - 79 CHD2 mortality -3.660036e+03
## 77 80 - 84 CHD2 mortality -4.346514e+03
## 78 15 - 19 Breast cancer -4.624153e-01
## 79 20 - 24 Breast cancer -1.587543e+00
## 80 25 - 29 Breast cancer -7.326368e+00
## 81 30 - 34 Breast cancer -2.873340e+01
## 82 35 - 39 Breast cancer -5.218217e+01
## 83 40 - 44 Breast cancer -8.577757e+01
## 84 45 - 49 Breast cancer -1.395958e+02
## 85 50 - 54 Breast cancer -2.070689e+02
## 86 55 - 59 Breast cancer -2.658472e+02
## 87 60 - 64 Breast cancer -2.998331e+02
## 88 65 - 69 Breast cancer -3.267095e+02
## 89 70 - 74 Breast cancer -3.327787e+02
## 90 75 - 79 Breast cancer -2.441402e+02
## 91 80 - 84 Breast cancer -1.884558e+02oprint(summary(BoDattr,marginals=c("Exposure_agent","Response"),"mean"))## Exposure_agent Response
## 1 TEQ Cancer morbidity yearly
## 2 TEQ Dioxin recommendation tolerable daily intake
## 3 TEQ Dioxin recommendation tolerable daily intake 2018
## 4 PFAS Immunosuppression
## 5 DHA Loss in child's IQ points
## 6 MeHg Loss in child's IQ points
## 7 PFAS PFAS TWI
## 8 TEQ Sperm concentration
## 9 Vitamin D Vitamin D recommendation
## 10 TEQ Yes or no dental defect
## 11 Fish All-cause mortality
## 12 Fish Depression
## 13 Omega3 CHD2 mortality
## 14 Omega3 Breast cancer
## mean
## 1 42.950665
## 2 -2165.611397
## 3 1776.911916
## 4 7.984307
## 5 -3292.013959
## 6 4686.945878
## 7 1370.934688
## 8 5024.148265
## 9 610.813471
## 10 2795.377581
## 11 -1771.969747
## 12 -228.525606
## 13 -1190.045495
## 14 -155.749901oprint(summary(BoDattr,marginals=c("Gender","Response"),"mean"))## Gender Response mean
## 1 Female Cancer morbidity yearly 31.196086
## 2 Male Cancer morbidity yearly 54.705243
## 3 Female Dioxin recommendation tolerable daily intake -2192.564681
## 4 Male Dioxin recommendation tolerable daily intake -2138.658113
## 5 Female Dioxin recommendation tolerable daily intake 2018 1799.027431
## 6 Male Dioxin recommendation tolerable daily intake 2018 1754.796401
## 7 Female Immunosuppression 5.957023
## 8 Male Immunosuppression 10.011591
## 9 Female Loss in child's IQ points 542.752772
## 10 Male Loss in child's IQ points 852.179148
## 11 Female PFAS TWI 0.000000
## 12 Male PFAS TWI 2741.869376
## 13 Female Sperm concentration 4004.275815
## 14 Male Sperm concentration 6044.020715
## 15 Female Vitamin D recommendation 618.415679
## 16 Male Vitamin D recommendation 603.211263
## 17 Female Yes or no dental defect 2227.932428
## 18 Male Yes or no dental defect 3362.822734
## 19 Female All-cause mortality -1003.248948
## 20 Male All-cause mortality -2540.690546
## 21 Female Depression -235.343301
## 22 Male Depression -221.707910
## 23 Female CHD2 mortality -815.643510
## 24 Male CHD2 mortality -1564.447480
## 25 Female Breast cancer -308.799252
## 26 Male Breast cancer -2.700550oprint(summary(case_burden,"mean"))## Response mean
## 1 Dioxin recommendation tolerable daily intake 0.001004988
## 2 Dioxin recommendation tolerable daily intake 2018 0.001004988
## 3 Loss in child's IQ points 0.110000000
## 4 PFAS TWI 0.001004988
## 5 Sperm concentration 2.500000000
## 6 Vitamin D recommendation 0.001004988
## 7 Yes or no dental defect 0.060000000oprint(summary(conc,"mean"))## Fish Compound mean
## 1 Average fish ALA 0.69000000
## 2 Bream ALA 0.22000000
## 3 Herring ALA 1.74000000
## 4 Pike ALA 0.08000000
## 5 Rainbow trout ALA 4.81000000
## 6 Roach ALA 0.10000000
## 7 Salmon ALA 7.96000000
## 8 Vendace ALA 1.35000000
## 9 Whitefish ALA 2.22000000
## 10 Average fish DHA 2.54000000
## 11 Bream DHA 2.73000000
## 12 Herring DHA 5.86000000
## 13 Pike DHA 0.30000000
## 14 Rainbow trout DHA 7.57000000
## 15 Roach DHA 2.87000000
## 16 Salmon DHA 6.69000000
## 17 Vendace DHA 3.00000000
## 18 Whitefish DHA 3.94000000
## 19 Average fish Fish 1.00000000
## 20 Bream Fish 1.00000000
## 21 Herring Fish 1.00000000
## 22 Pike Fish 1.00000000
## 23 Rainbow trout Fish 1.00000000
## 24 Roach Fish 1.00000000
## 25 Salmon Fish 1.00000000
## 26 Vendace Fish 1.00000000
## 27 Whitefish Fish 1.00000000
## 28 Average fish MeHg 0.11413395
## 29 Bream MeHg 0.63339772
## 30 Burbot MeHg 1.02686230
## 31 Chaetiliidae MeHg 0.01634649
## 32 Flounder MeHg 0.04582576
## 33 Fourhorn sculpin MeHg 0.64990137
## 34 Herring MeHg 0.02680994
## 35 Perch MeHg 0.14874312
## 36 Pike MeHg 0.90310953
## 37 Pike-perch MeHg 0.25432475
## 38 Roach MeHg 0.26739658
## 39 Sprat MeHg 0.03187976
## 40 White bream MeHg 1.21073890
## 41 Whitefish MeHg 0.06619502
## 42 Average fish Omega3 7.00000000
## 43 Bream Omega3 6.00000000
## 44 Herring Omega3 24.00000000
## 45 Pike Omega3 0.50000000
## 46 Rainbow trout Omega3 18.00000000
## 47 Roach Omega3 5.00000000
## 48 Salmon Omega3 23.00000000
## 49 Vendace Omega3 10.00000000
## 50 Whitefish Omega3 10.00000000
## 51 Average fish PFAS 8.26000000
## 52 Bream PFAS 4.95000000
## 53 Eel PFAS 7.89500000
## 54 Herring PFAS 1.65500000
## 55 Perch PFAS 5.12000000
## 56 Pike PFAS 7.50500000
## 57 Pike-perch PFAS 2.59500000
## 58 Rainbow trout PFAS 27.74000000
## 59 Roach PFAS 27.72000000
## 60 Salmon PFAS 3.00000000
## 61 Vendace PFAS 4.21000000
## 62 Whitefish PFAS 4.22000000
## 63 Baltic herring TEQ 90.08722156
## 64 Bream TEQ 6.20175433
## 65 Burbot TEQ 0.82549312
## 66 Cod TEQ 1.67367981
## 67 Flounder TEQ 15.36558024
## 68 Perch TEQ 3.56897420
## 69 Pike TEQ 2.14862962
## 70 Pike-perch TEQ 2.70814568
## 71 River lamprey TEQ 7.34301416
## 72 Roach TEQ 0.72124877
## 73 Salmon TEQ 20.00778296
## 74 Sea trout TEQ 9.31778459
## 75 Sprat TEQ 4.15456559
## 76 Vendace TEQ 0.99253888
## 77 Whitefish TEQ 5.74967872
## 78 Average fish Vitamin D 0.10500000
## 79 Bream Vitamin D 0.14000000
## 80 Herring Vitamin D 0.15600000
## 81 Pike Vitamin D 0.02100000
## 82 Rainbow trout Vitamin D 0.05100000
## 83 Roach Vitamin D 0.10000000
## 84 Salmon Vitamin D 0.06700000
## 85 Vendace Vitamin D 0.09400000
## 86 Whitefish Vitamin D 0.14400000oprint(summary(dose,"mean"))## Scenario Gender Exposure_agent Exposure Scaling mean
## 1 BAU Female ALA Direct BW 1.56247954
## 2 BAU Male ALA Direct BW 2.34371931
## 3 BAU Female DHA Direct BW 2.04671635
## 4 BAU Male DHA Direct BW 3.07007453
## 5 BAU Female Fish Direct BW 0.44440477
## 6 BAU Male Fish Direct BW 0.66660715
## 7 BAU Female MeHg Direct BW 0.02705835
## 8 BAU Male MeHg Direct BW 0.04058752
## 9 BAU Female Omega3 Direct BW 6.13267239
## 10 BAU Male Omega3 Direct BW 9.19900859
## 11 BAU Female PFAS Direct BW 4.16332442
## 12 BAU Male PFAS Direct BW 6.24498663
## 13 BAU Female TEQ Direct BW 2.69609423
## 14 BAU Male TEQ Direct BW 4.04414134
## 15 BAU Female Vitamin D Direct BW 0.03865415
## 16 BAU Male Vitamin D Direct BW 0.05798122
## 17 BAU Female PFAS To child BW 0.23415010
## 18 BAU Male PFAS To child BW 0.23415010
## 19 BAU Female TEQ To child BW 0.35380662
## 20 BAU Male TEQ To child BW 0.35380662
## 21 BAU Female ALA Direct Log10 2.03891238
## 22 BAU Male ALA Direct Log10 2.21500364
## 23 BAU Female DHA Direct Log10 2.15615570
## 24 BAU Male DHA Direct Log10 2.33224696
## 25 BAU Female Fish Direct Log10 1.49287675
## 26 BAU Male Fish Direct Log10 1.66896801
## 27 BAU Female MeHg Direct Log10 0.27739929
## 28 BAU Male MeHg Direct Log10 0.45349055
## 29 BAU Female Omega3 Direct Log10 2.63274781
## 30 BAU Male Omega3 Direct Log10 2.80883906
## 31 BAU Female PFAS Direct Log10 2.46453829
## 32 BAU Male PFAS Direct Log10 2.64062955
## 33 BAU Female TEQ Direct Log10 2.27583311
## 34 BAU Male TEQ Direct Log10 2.45192437
## 35 BAU Female Vitamin D Direct Log10 0.43229411
## 36 BAU Male Vitamin D Direct Log10 0.60838537
## 37 BAU Female PFAS To child Log10 1.21459239
## 38 BAU Male PFAS To child Log10 1.21459239
## 39 BAU Female TEQ To child Log10 1.39386399
## 40 BAU Male TEQ To child Log10 1.39386399
## 41 BAU Female ALA Direct None 109.37356784
## 42 BAU Male ALA Direct None 164.06035175
## 43 BAU Female DHA Direct None 143.27014453
## 44 BAU Male DHA Direct None 214.90521680
## 45 BAU Female Fish Direct None 31.10833358
## 46 BAU Male Fish Direct None 46.66250037
## 47 BAU Female MeHg Direct None 1.89408422
## 48 BAU Male MeHg Direct None 2.84112633
## 49 BAU Female Omega3 Direct None 429.28706762
## 50 BAU Male Omega3 Direct None 643.93060143
## 51 BAU Female PFAS Direct None 291.43270938
## 52 BAU Male PFAS Direct None 437.14906407
## 53 BAU Female TEQ Direct None 188.72659592
## 54 BAU Male TEQ Direct None 283.08989388
## 55 BAU Female Vitamin D Direct None 2.70579016
## 56 BAU Male Vitamin D Direct None 4.05868524
## 57 BAU Female PFAS To child None 16.39050716
## 58 BAU Male PFAS To child None 16.39050716
## 59 BAU Female TEQ To child None 24.76646312
## 60 BAU Male TEQ To child None 24.76646312oprint(summary(ERF,"mean"))## Exposure_agent Response
## 1 Fish All-cause mortality
## 2 Omega3 Breast cancer
## 3 Fish Depression
## 4 DHA Loss in child's IQ points
## 5 TEQ Sperm concentration
## 6 TEQ Yes or no dental defect
## 7 Omega3 CHD2 mortality
## 8 Vitamin D Vitamin D recommendation
## 9 MeHg Loss in child's IQ points
## 10 PFAS Immunosuppression
## 11 PFAS PFAS TWI
## 12 TEQ Cancer morbidity yearly
## 13 TEQ Dioxin recommendation tolerable daily intake
## 14 TEQ Dioxin recommendation tolerable daily intake 2018
## 15 Fish All-cause mortality
## 16 Omega3 Breast cancer
## 17 Fish Depression
## 18 DHA Loss in child's IQ points
## 19 TEQ Sperm concentration
## 20 TEQ Yes or no dental defect
## 21 Omega3 CHD2 mortality
## 22 Vitamin D Vitamin D recommendation
## 23 MeHg Loss in child's IQ points
## 24 PFAS Immunosuppression
## 25 PFAS PFAS TWI
## 26 TEQ Cancer morbidity yearly
## 27 TEQ Dioxin recommendation tolerable daily intake
## 28 TEQ Dioxin recommendation tolerable daily intake 2018
## ER_function Scaling Observation mean
## 1 RR None ERF 0.997871700
## 2 RR None ERF 0.999487200
## 3 RR None ERF 0.994690400
## 4 ERS None ERF -0.001300000
## 5 ERS None ERF 0.000060000
## 6 ERS None ERF 0.001390971
## 7 Relative Hill None ERF -0.170000000
## 8 Step None ERF 100.000000000
## 9 ERS BW ERF 9.800000000
## 10 ERS BW ERF 0.022700000
## 11 TWI BW ERF 4.400000000
## 12 CSF BW ERF 0.000010000
## 13 TDI BW ERF 2.000000000
## 14 TDI BW ERF 0.288900000
## 15 RR None Threshold 0.000000000
## 16 RR None Threshold 0.000000000
## 17 RR None Threshold 0.000000000
## 18 ERS None Threshold 0.000000000
## 19 ERS None Threshold 0.000000000
## 20 ERS None Threshold 0.000000000
## 21 Relative Hill None Threshold 47.000000000
## 22 Step None Threshold 10.000000000
## 23 ERS BW Threshold 0.000000000
## 24 ERS BW Threshold 0.000000000
## 25 TWI BW Threshold 0.000000000
## 26 CSF BW Threshold 0.000000000
## 27 TDI BW Threshold 0.000000000
## 28 TDI BW Threshold 0.000000000oprint(summary(expo_dir,"mean"))## Kala Scenario Gender Fish Exposure_agent mean
## 1 Kaupallinen BAU Female Average fish ALA 0.87611225
## 2 Muu tuonti BAU Female Average fish ALA 7.66598220
## 3 Vapaa-ajan BAU Female Average fish ALA 2.71047228
## 4 Kaupallinen BAU Male Average fish ALA 1.31416838
## 5 Muu tuonti BAU Male Average fish ALA 11.49897331
## 6 Vapaa-ajan BAU Male Average fish ALA 4.06570842
## 7 Silakka BAU Female Herring ALA 1.10466327
## 8 Silakka BAU Male Herring ALA 1.65699491
## 9 Kirjolohi BAU Female Rainbow trout ALA 12.78735034
## 10 Tuontikirjolohi BAU Female Rainbow trout ALA 9.16108681
## 11 Kirjolohi BAU Male Rainbow trout ALA 19.18102550
## 12 Tuontikirjolohi BAU Male Rainbow trout ALA 13.74163021
## 13 Tuontilohi BAU Female Salmon ALA 74.53937644
## 14 Tuontilohi BAU Male Salmon ALA 111.80906467
## 15 Kasvatettu BAU Female Whitefish ALA 0.52852424
## 16 Kasvatettu BAU Male Whitefish ALA 0.79278636
## 17 Kaupallinen BAU Female Average fish DHA 3.22510887
## 18 Muu tuonti BAU Female Average fish DHA 28.21970261
## 19 Vapaa-ajan BAU Female Average fish DHA 9.97768056
## 20 Kaupallinen BAU Male Average fish DHA 4.83766330
## 21 Muu tuonti BAU Male Average fish DHA 42.32955391
## 22 Vapaa-ajan BAU Male Average fish DHA 14.96652085
## 23 Silakka BAU Female Herring DHA 3.72030275
## 24 Silakka BAU Male Herring DHA 5.58045413
## 25 Kirjolohi BAU Female Rainbow trout DHA 20.12479045
## 26 Tuontikirjolohi BAU Female Rainbow trout DHA 14.41776032
## 27 Kirjolohi BAU Male Rainbow trout DHA 30.18718567
## 28 Tuontikirjolohi BAU Male Rainbow trout DHA 21.62664048
## 29 Tuontilohi BAU Female Salmon DHA 62.64678749
## 30 Tuontilohi BAU Male Salmon DHA 93.97018123
## 31 Kasvatettu BAU Female Whitefish DHA 0.93801149
## 32 Kasvatettu BAU Male Whitefish DHA 1.40701723
## 33 Kaupallinen BAU Female Average fish Fish 1.26972790
## 34 Muu tuonti BAU Female Average fish Fish 11.11011914
## 35 Vapaa-ajan BAU Female Average fish Fish 3.92822069
## 36 Kaupallinen BAU Male Average fish Fish 1.90459185
## 37 Muu tuonti BAU Male Average fish Fish 16.66517870
## 38 Vapaa-ajan BAU Male Average fish Fish 5.89233104
## 39 Silakka BAU Female Herring Fish 0.63486395
## 40 Silakka BAU Male Herring Fish 0.95229593
## 41 Kirjolohi BAU Female Rainbow trout Fish 2.65849279
## 42 Tuontikirjolohi BAU Female Rainbow trout Fish 1.90459185
## 43 Kirjolohi BAU Male Rainbow trout Fish 3.98773919
## 44 Tuontikirjolohi BAU Male Rainbow trout Fish 2.85688778
## 45 Tuontilohi BAU Female Salmon Fish 9.36424327
## 46 Tuontilohi BAU Male Salmon Fish 14.04636491
## 47 Kasvatettu BAU Female Whitefish Fish 0.23807398
## 48 Kasvatettu BAU Male Whitefish Fish 0.35711097
## 49 Kaupallinen BAU Female Average fish MeHg 0.14491907
## 50 Muu tuonti BAU Female Average fish MeHg 1.26804182
## 51 Vapaa-ajan BAU Female Average fish MeHg 0.44834336
## 52 Kaupallinen BAU Male Average fish MeHg 0.21737860
## 53 Muu tuonti BAU Male Average fish MeHg 1.90206273
## 54 Vapaa-ajan BAU Male Average fish MeHg 0.67251504
## 55 Silakka BAU Female Herring MeHg 0.01702066
## 56 Silakka BAU Male Herring MeHg 0.02553100
## 57 Kasvatettu BAU Female Whitefish MeHg 0.01575931
## 58 Kasvatettu BAU Male Whitefish MeHg 0.02363897
## 59 Kaupallinen BAU Female Average fish Omega3 8.88809531
## 60 Muu tuonti BAU Female Average fish Omega3 77.77083395
## 61 Vapaa-ajan BAU Female Average fish Omega3 27.49754486
## 62 Kaupallinen BAU Male Average fish Omega3 13.33214296
## 63 Muu tuonti BAU Male Average fish Omega3 116.65625093
## 64 Vapaa-ajan BAU Male Average fish Omega3 41.24631729
## 65 Silakka BAU Female Herring Omega3 15.23673482
## 66 Silakka BAU Male Herring Omega3 22.85510222
## 67 Kirjolohi BAU Female Rainbow trout Omega3 47.85287028
## 68 Tuontikirjolohi BAU Female Rainbow trout Omega3 34.28265333
## 69 Kirjolohi BAU Male Rainbow trout Omega3 71.77930542
## 70 Tuontikirjolohi BAU Male Rainbow trout Omega3 51.42398000
## 71 Tuontilohi BAU Female Salmon Omega3 215.37759525
## 72 Tuontilohi BAU Male Salmon Omega3 323.06639288
## 73 Kasvatettu BAU Female Whitefish Omega3 2.38073981
## 74 Kasvatettu BAU Male Whitefish Omega3 3.57110972
## 75 Kaupallinen BAU Female Average fish PFAS 10.48795246
## 76 Muu tuonti BAU Female Average fish PFAS 91.76958406
## 77 Vapaa-ajan BAU Female Average fish PFAS 32.44710294
## 78 Kaupallinen BAU Male Average fish PFAS 15.73192870
## 79 Muu tuonti BAU Male Average fish PFAS 137.65437610
## 80 Vapaa-ajan BAU Male Average fish PFAS 48.67065441
## 81 Silakka BAU Female Herring PFAS 1.05069984
## 82 Silakka BAU Male Herring PFAS 1.57604976
## 83 Kirjolohi BAU Female Rainbow trout PFAS 73.74659009
## 84 Tuontikirjolohi BAU Female Rainbow trout PFAS 52.83337797
## 85 Kirjolohi BAU Male Rainbow trout PFAS 110.61988513
## 86 Tuontikirjolohi BAU Male Rainbow trout PFAS 79.25006696
## 87 Tuontilohi BAU Female Salmon PFAS 28.09272982
## 88 Tuontilohi BAU Male Salmon PFAS 42.13909472
## 89 Kasvatettu BAU Female Whitefish PFAS 1.00467220
## 90 Kasvatettu BAU Male Whitefish PFAS 1.50700830
## 91 Tuontilohi BAU Female Salmon TEQ 187.35774701
## 92 Tuontilohi BAU Male Salmon TEQ 281.03662052
## 93 Kasvatettu BAU Female Whitefish TEQ 1.36884891
## 94 Kasvatettu BAU Male Whitefish TEQ 2.05327336
## 95 Kaupallinen BAU Female Average fish Vitamin D 0.13332143
## 96 Muu tuonti BAU Female Average fish Vitamin D 1.16656251
## 97 Vapaa-ajan BAU Female Average fish Vitamin D 0.41246317
## 98 Kaupallinen BAU Male Average fish Vitamin D 0.19998214
## 99 Muu tuonti BAU Male Average fish Vitamin D 1.74984376
## 100 Vapaa-ajan BAU Male Average fish Vitamin D 0.61869476
## 101 Silakka BAU Female Herring Vitamin D 0.09903878
## 102 Silakka BAU Male Herring Vitamin D 0.14855816
## 103 Kirjolohi BAU Female Rainbow trout Vitamin D 0.13558313
## 104 Tuontikirjolohi BAU Female Rainbow trout Vitamin D 0.09713418
## 105 Kirjolohi BAU Male Rainbow trout Vitamin D 0.20337470
## 106 Tuontikirjolohi BAU Male Rainbow trout Vitamin D 0.14570128
## 107 Tuontilohi BAU Female Salmon Vitamin D 0.62740430
## 108 Tuontilohi BAU Male Salmon Vitamin D 0.94110645
## 109 Kasvatettu BAU Female Whitefish Vitamin D 0.03428265
## 110 Kasvatettu BAU Male Whitefish Vitamin D 0.05142398oprint(summary(expo_indir,"mean"))## Kala Scenario Gender Fish Exposure_agent Exposure
## 1 Kaupallinen BAU Female Average fish PFAS To child
## 2 Muu tuonti BAU Female Average fish PFAS To child
## 3 Vapaa-ajan BAU Female Average fish PFAS To child
## 4 Kaupallinen BAU Male Average fish PFAS To child
## 5 Muu tuonti BAU Male Average fish PFAS To child
## 6 Vapaa-ajan BAU Male Average fish PFAS To child
## 7 Silakka BAU Female Herring PFAS To child
## 8 Silakka BAU Male Herring PFAS To child
## 9 Kirjolohi BAU Female Rainbow trout PFAS To child
## 10 Tuontikirjolohi BAU Female Rainbow trout PFAS To child
## 11 Kirjolohi BAU Male Rainbow trout PFAS To child
## 12 Tuontikirjolohi BAU Male Rainbow trout PFAS To child
## 13 Tuontilohi BAU Female Salmon PFAS To child
## 14 Tuontilohi BAU Male Salmon PFAS To child
## 15 Kasvatettu BAU Female Whitefish PFAS To child
## 16 Kasvatettu BAU Male Whitefish PFAS To child
## 17 Tuontilohi BAU Female Salmon TEQ To child
## 18 Tuontilohi BAU Male Salmon TEQ To child
## 19 Kasvatettu BAU Female Whitefish TEQ To child
## 20 Kasvatettu BAU Male Whitefish TEQ To child
## mean
## 1 0.58985438
## 2 5.16122582
## 3 1.82486199
## 4 0.88478157
## 5 7.74183873
## 6 2.73729298
## 7 0.05909255
## 8 0.08863883
## 9 4.14759213
## 10 2.97140929
## 11 6.22138820
## 12 4.45711393
## 13 1.57996709
## 14 2.36995063
## 15 0.05650391
## 16 0.08475586
## 17 24.58683001
## 18 36.88024502
## 19 0.17963311
## 20 0.26944967oprint(summary(exposure,"mean"))## Scenario Gender Exposure_agent Exposure mean
## 1 BAU Female ALA Direct 109.373568
## 2 BAU Male ALA Direct 164.060352
## 3 BAU Female DHA Direct 143.270145
## 4 BAU Male DHA Direct 214.905217
## 5 BAU Female Fish Direct 31.108334
## 6 BAU Male Fish Direct 46.662500
## 7 BAU Female MeHg Direct 1.894084
## 8 BAU Male MeHg Direct 2.841126
## 9 BAU Female Omega3 Direct 429.287068
## 10 BAU Male Omega3 Direct 643.930601
## 11 BAU Female PFAS Direct 291.432709
## 12 BAU Male PFAS Direct 437.149064
## 13 BAU Female TEQ Direct 188.726596
## 14 BAU Male TEQ Direct 283.089894
## 15 BAU Female Vitamin D Direct 2.705790
## 16 BAU Male Vitamin D Direct 4.058685
## 17 BAU Female PFAS To child 16.390507
## 18 BAU Male PFAS To child 16.390507
## 19 BAU Female TEQ To child 24.766463
## 20 BAU Male TEQ To child 24.766463#oprint(summary(fish_proportion,"mean"))
oprint(summary(incidence,"mean"))## Response Age Adjust mean
## 1 Immunosuppression 0 - 4 BAU 4.8100
## 2 Loss in child's IQ points 0 - 4 BAU 1.1920
## 3 Sperm concentration 0 - 4 BAU 0.0140
## 4 Yes or no dental defect 0 - 4 BAU 0.0448
## 5 Immunosuppression 5 - 9 BAU 3.9500
## 6 Dioxin recommendation tolerable daily intake Undefined BAU 0.1100
## 7 Dioxin recommendation tolerable daily intake 2018 Undefined BAU 0.3200
## 8 PFAS TWI Undefined BAU 1.0000
## 9 Vitamin D recommendation Undefined BAU 0.2200oprint(summary(PAF,"mean"))## Exposure_agent Response Scenario
## 1 DHA Loss in child's IQ points BAU
## 2 MeHg Loss in child's IQ points BAU
## 3 TEQ Cancer morbidity yearly BAU
## 4 TEQ Sperm concentration BAU
## 5 TEQ Yes or no dental defect BAU
## 6 PFAS Immunosuppression BAU
## 7 Fish All-cause mortality BAU
## 8 Omega3 Breast cancer BAU
## 9 Fish Depression BAU
## 10 Omega3 CHD2 mortality BAU
## 11 DHA Loss in child's IQ points BAU
## 12 MeHg Loss in child's IQ points BAU
## 13 TEQ Cancer morbidity yearly BAU
## 14 TEQ Sperm concentration BAU
## 15 TEQ Yes or no dental defect BAU
## 16 PFAS Immunosuppression BAU
## 17 Fish All-cause mortality BAU
## 18 Omega3 Breast cancer BAU
## 19 Fish Depression BAU
## 20 Omega3 CHD2 mortality BAU
## 21 TEQ Cancer morbidity yearly BAU
## 22 Fish All-cause mortality BAU
## 23 Omega3 Breast cancer BAU
## 24 Fish Depression BAU
## 25 Omega3 CHD2 mortality BAU
## 26 TEQ Cancer morbidity yearly BAU
## 27 Fish All-cause mortality BAU
## 28 Omega3 Breast cancer BAU
## 29 Fish Depression BAU
## 30 Omega3 CHD2 mortality BAU
## 31 TEQ Cancer morbidity yearly BAU
## 32 Fish All-cause mortality BAU
## 33 Omega3 Breast cancer BAU
## 34 Fish Depression BAU
## 35 Omega3 CHD2 mortality BAU
## 36 TEQ Cancer morbidity yearly BAU
## 37 Fish All-cause mortality BAU
## 38 Omega3 Breast cancer BAU
## 39 Fish Depression BAU
## 40 Omega3 CHD2 mortality BAU
## 41 TEQ Cancer morbidity yearly BAU
## 42 Fish All-cause mortality BAU
## 43 Omega3 Breast cancer BAU
## 44 Fish Depression BAU
## 45 Omega3 CHD2 mortality BAU
## 46 TEQ Cancer morbidity yearly BAU
## 47 Fish All-cause mortality BAU
## 48 Omega3 Breast cancer BAU
## 49 Fish Depression BAU
## 50 Omega3 CHD2 mortality BAU
## 51 TEQ Cancer morbidity yearly BAU
## 52 Fish All-cause mortality BAU
## 53 Omega3 Breast cancer BAU
## 54 Fish Depression BAU
## 55 Omega3 CHD2 mortality BAU
## 56 TEQ Cancer morbidity yearly BAU
## 57 Fish All-cause mortality BAU
## 58 Omega3 Breast cancer BAU
## 59 Fish Depression BAU
## 60 Omega3 CHD2 mortality BAU
## 61 TEQ Cancer morbidity yearly BAU
## 62 Fish All-cause mortality BAU
## 63 Omega3 Breast cancer BAU
## 64 Fish Depression BAU
## 65 Omega3 CHD2 mortality BAU
## 66 TEQ Cancer morbidity yearly BAU
## 67 Fish All-cause mortality BAU
## 68 Omega3 Breast cancer BAU
## 69 Fish Depression BAU
## 70 Omega3 CHD2 mortality BAU
## 71 TEQ Cancer morbidity yearly BAU
## 72 Fish All-cause mortality BAU
## 73 Omega3 Breast cancer BAU
## 74 Fish Depression BAU
## 75 Omega3 CHD2 mortality BAU
## 76 TEQ Cancer morbidity yearly BAU
## 77 Fish All-cause mortality BAU
## 78 Omega3 Breast cancer BAU
## 79 Fish Depression BAU
## 80 Omega3 CHD2 mortality BAU
## 81 TEQ Cancer morbidity yearly BAU
## 82 Fish All-cause mortality BAU
## 83 Omega3 Breast cancer BAU
## 84 Fish Depression BAU
## 85 Omega3 CHD2 mortality BAU
## 86 TEQ Cancer morbidity yearly BAU
## 87 Fish All-cause mortality BAU
## 88 Omega3 Breast cancer BAU
## 89 Fish Depression BAU
## 90 Omega3 CHD2 mortality BAU
## 91 TEQ Cancer morbidity yearly BAU
## 92 Fish All-cause mortality BAU
## 93 Omega3 Breast cancer BAU
## 94 Fish Depression BAU
## 95 Omega3 CHD2 mortality BAU
## 96 TEQ Cancer morbidity yearly BAU
## 97 Fish All-cause mortality BAU
## 98 Omega3 Breast cancer BAU
## 99 Fish Depression BAU
## 100 Omega3 CHD2 mortality BAU
## 101 TEQ Cancer morbidity yearly BAU
## 102 PFAS Immunosuppression BAU
## 103 Fish All-cause mortality BAU
## 104 Omega3 Breast cancer BAU
## 105 Fish Depression BAU
## 106 Omega3 CHD2 mortality BAU
## 107 TEQ Cancer morbidity yearly BAU
## 108 PFAS Immunosuppression BAU
## 109 Fish All-cause mortality BAU
## 110 Omega3 Breast cancer BAU
## 111 Fish Depression BAU
## 112 Omega3 CHD2 mortality BAU
## 113 TEQ Cancer morbidity yearly BAU
## 114 Fish All-cause mortality BAU
## 115 Omega3 Breast cancer BAU
## 116 Fish Depression BAU
## 117 Omega3 CHD2 mortality BAU
## 118 TEQ Cancer morbidity yearly BAU
## 119 Fish All-cause mortality BAU
## 120 Omega3 Breast cancer BAU
## 121 Fish Depression BAU
## 122 Omega3 CHD2 mortality BAU
## 123 TEQ Cancer morbidity yearly BAU
## 124 Fish All-cause mortality BAU
## 125 Omega3 Breast cancer BAU
## 126 Fish Depression BAU
## 127 Omega3 CHD2 mortality BAU
## 128 TEQ Cancer morbidity yearly BAU
## 129 Fish All-cause mortality BAU
## 130 Omega3 Breast cancer BAU
## 131 Fish Depression BAU
## 132 Omega3 CHD2 mortality BAU
## 133 TEQ Cancer morbidity yearly BAU
## 134 Fish All-cause mortality BAU
## 135 Omega3 Breast cancer BAU
## 136 Fish Depression BAU
## 137 Omega3 CHD2 mortality BAU
## 138 TEQ Cancer morbidity yearly BAU
## 139 Fish All-cause mortality BAU
## 140 Omega3 Breast cancer BAU
## 141 Fish Depression BAU
## 142 Omega3 CHD2 mortality BAU
## 143 TEQ Cancer morbidity yearly BAU
## 144 Fish All-cause mortality BAU
## 145 Omega3 Breast cancer BAU
## 146 Fish Depression BAU
## 147 Omega3 CHD2 mortality BAU
## 148 TEQ Cancer morbidity yearly BAU
## 149 Fish All-cause mortality BAU
## 150 Omega3 Breast cancer BAU
## 151 Fish Depression BAU
## 152 Omega3 CHD2 mortality BAU
## 153 TEQ Cancer morbidity yearly BAU
## 154 Fish All-cause mortality BAU
## 155 Omega3 Breast cancer BAU
## 156 Fish Depression BAU
## 157 Omega3 CHD2 mortality BAU
## 158 TEQ Cancer morbidity yearly BAU
## 159 Fish All-cause mortality BAU
## 160 Omega3 Breast cancer BAU
## 161 Fish Depression BAU
## 162 Omega3 CHD2 mortality BAU
## 163 TEQ Cancer morbidity yearly BAU
## 164 Fish All-cause mortality BAU
## 165 Omega3 Breast cancer BAU
## 166 Fish Depression BAU
## 167 Omega3 CHD2 mortality BAU
## 168 TEQ Cancer morbidity yearly BAU
## 169 Fish All-cause mortality BAU
## 170 Omega3 Breast cancer BAU
## 171 Fish Depression BAU
## 172 Omega3 CHD2 mortality BAU
## 173 TEQ Cancer morbidity yearly BAU
## 174 Fish All-cause mortality BAU
## 175 Omega3 Breast cancer BAU
## 176 Fish Depression BAU
## 177 Omega3 CHD2 mortality BAU
## 178 TEQ Cancer morbidity yearly BAU
## 179 Fish All-cause mortality BAU
## 180 Omega3 Breast cancer BAU
## 181 Fish Depression BAU
## 182 Omega3 CHD2 mortality BAU
## 183 TEQ Cancer morbidity yearly BAU
## 184 Fish All-cause mortality BAU
## 185 Omega3 Breast cancer BAU
## 186 Fish Depression BAU
## 187 Omega3 CHD2 mortality BAU
## 188 TEQ Cancer morbidity yearly BAU
## 189 Fish All-cause mortality BAU
## 190 Omega3 Breast cancer BAU
## 191 Fish Depression BAU
## 192 Omega3 CHD2 mortality BAU
## 193 TEQ Cancer morbidity yearly BAU
## 194 TEQ Dioxin recommendation tolerable daily intake BAU
## 195 TEQ Dioxin recommendation tolerable daily intake 2018 BAU
## 196 Vitamin D Vitamin D recommendation BAU
## 197 PFAS PFAS TWI BAU
## 198 Fish All-cause mortality BAU
## 199 Omega3 Breast cancer BAU
## 200 Fish Depression BAU
## 201 Omega3 CHD2 mortality BAU
## 202 TEQ Cancer morbidity yearly BAU
## 203 TEQ Dioxin recommendation tolerable daily intake BAU
## 204 TEQ Dioxin recommendation tolerable daily intake 2018 BAU
## 205 Vitamin D Vitamin D recommendation BAU
## 206 PFAS PFAS TWI BAU
## 207 Fish All-cause mortality BAU
## 208 Omega3 Breast cancer BAU
## 209 Fish Depression BAU
## 210 Omega3 CHD2 mortality BAU
## Gender Age Adjust mean
## 1 Female 0 - 4 BAU -0.156250997
## 2 Female 0 - 4 BAU 0.222459556
## 3 Female 0 - 4 BAU 0.004642163
## 4 Female 0 - 4 BAU 0.914970253
## 5 Female 0 - 4 BAU 6.628630113
## 6 Female 0 - 4 BAU 0.020753154
## 7 Female 0 - 4 BAU -0.064129738
## 8 Female 0 - 4 BAU -0.197637574
## 9 Female 0 - 4 BAU -0.152625783
## 10 Female 0 - 4 BAU -0.153224403
## 11 Male 0 - 4 BAU -0.234376495
## 12 Male 0 - 4 BAU 0.333689334
## 13 Male 0 - 4 BAU 0.006693985
## 14 Male 0 - 4 BAU 1.319384387
## 15 Male 0 - 4 BAU 9.558464934
## 16 Male 0 - 4 BAU 0.030577215
## 17 Male 0 - 4 BAU -0.094635480
## 18 Male 0 - 4 BAU -0.281286382
## 19 Male 0 - 4 BAU -0.219967194
## 20 Male 0 - 4 BAU -0.158435886
## 21 Female 10 - 14 BAU 0.004642163
## 22 Female 10 - 14 BAU -0.064129738
## 23 Female 10 - 14 BAU -0.197637574
## 24 Female 10 - 14 BAU -0.152625783
## 25 Female 10 - 14 BAU -0.153224403
## 26 Male 10 - 14 BAU 0.006693985
## 27 Male 10 - 14 BAU -0.094635480
## 28 Male 10 - 14 BAU -0.281286382
## 29 Male 10 - 14 BAU -0.219967194
## 30 Male 10 - 14 BAU -0.158435886
## 31 Female 15 - 19 BAU 0.004642163
## 32 Female 15 - 19 BAU -0.064129738
## 33 Female 15 - 19 BAU -0.197637574
## 34 Female 15 - 19 BAU -0.152625783
## 35 Female 15 - 19 BAU -0.153224403
## 36 Male 15 - 19 BAU 0.006693985
## 37 Male 15 - 19 BAU -0.094635480
## 38 Male 15 - 19 BAU -0.281286382
## 39 Male 15 - 19 BAU -0.219967194
## 40 Male 15 - 19 BAU -0.158435886
## 41 Female 20 - 24 BAU 0.004642163
## 42 Female 20 - 24 BAU -0.064129738
## 43 Female 20 - 24 BAU -0.197637574
## 44 Female 20 - 24 BAU -0.152625783
## 45 Female 20 - 24 BAU -0.153224403
## 46 Male 20 - 24 BAU 0.006693985
## 47 Male 20 - 24 BAU -0.094635480
## 48 Male 20 - 24 BAU -0.281286382
## 49 Male 20 - 24 BAU -0.219967194
## 50 Male 20 - 24 BAU -0.158435886
## 51 Female 25 - 29 BAU 0.004642163
## 52 Female 25 - 29 BAU -0.064129738
## 53 Female 25 - 29 BAU -0.197637574
## 54 Female 25 - 29 BAU -0.152625783
## 55 Female 25 - 29 BAU -0.153224403
## 56 Male 25 - 29 BAU 0.006693985
## 57 Male 25 - 29 BAU -0.094635480
## 58 Male 25 - 29 BAU -0.281286382
## 59 Male 25 - 29 BAU -0.219967194
## 60 Male 25 - 29 BAU -0.158435886
## 61 Female 30 - 34 BAU 0.004642163
## 62 Female 30 - 34 BAU -0.064129738
## 63 Female 30 - 34 BAU -0.197637574
## 64 Female 30 - 34 BAU -0.152625783
## 65 Female 30 - 34 BAU -0.153224403
## 66 Male 30 - 34 BAU 0.006693985
## 67 Male 30 - 34 BAU -0.094635480
## 68 Male 30 - 34 BAU -0.281286382
## 69 Male 30 - 34 BAU -0.219967194
## 70 Male 30 - 34 BAU -0.158435886
## 71 Female 35 - 39 BAU 0.004642163
## 72 Female 35 - 39 BAU -0.064129738
## 73 Female 35 - 39 BAU -0.197637574
## 74 Female 35 - 39 BAU -0.152625783
## 75 Female 35 - 39 BAU -0.153224403
## 76 Male 35 - 39 BAU 0.006693985
## 77 Male 35 - 39 BAU -0.094635480
## 78 Male 35 - 39 BAU -0.281286382
## 79 Male 35 - 39 BAU -0.219967194
## 80 Male 35 - 39 BAU -0.158435886
## 81 Female 40 - 44 BAU 0.004642163
## 82 Female 40 - 44 BAU -0.064129738
## 83 Female 40 - 44 BAU -0.197637574
## 84 Female 40 - 44 BAU -0.152625783
## 85 Female 40 - 44 BAU -0.153224403
## 86 Male 40 - 44 BAU 0.006693985
## 87 Male 40 - 44 BAU -0.094635480
## 88 Male 40 - 44 BAU -0.281286382
## 89 Male 40 - 44 BAU -0.219967194
## 90 Male 40 - 44 BAU -0.158435886
## 91 Female 45 - 49 BAU 0.004642163
## 92 Female 45 - 49 BAU -0.064129738
## 93 Female 45 - 49 BAU -0.197637574
## 94 Female 45 - 49 BAU -0.152625783
## 95 Female 45 - 49 BAU -0.153224403
## 96 Male 45 - 49 BAU 0.006693985
## 97 Male 45 - 49 BAU -0.094635480
## 98 Male 45 - 49 BAU -0.281286382
## 99 Male 45 - 49 BAU -0.219967194
## 100 Male 45 - 49 BAU -0.158435886
## 101 Female 5 - 9 BAU 0.004642163
## 102 Female 5 - 9 BAU 0.025271562
## 103 Female 5 - 9 BAU -0.064129738
## 104 Female 5 - 9 BAU -0.197637574
## 105 Female 5 - 9 BAU -0.152625783
## 106 Female 5 - 9 BAU -0.153224403
## 107 Male 5 - 9 BAU 0.006693985
## 108 Male 5 - 9 BAU 0.037234533
## 109 Male 5 - 9 BAU -0.094635480
## 110 Male 5 - 9 BAU -0.281286382
## 111 Male 5 - 9 BAU -0.219967194
## 112 Male 5 - 9 BAU -0.158435886
## 113 Female 50 - 54 BAU 0.004642163
## 114 Female 50 - 54 BAU -0.064129738
## 115 Female 50 - 54 BAU -0.197637574
## 116 Female 50 - 54 BAU -0.152625783
## 117 Female 50 - 54 BAU -0.153224403
## 118 Male 50 - 54 BAU 0.006693985
## 119 Male 50 - 54 BAU -0.094635480
## 120 Male 50 - 54 BAU -0.281286382
## 121 Male 50 - 54 BAU -0.219967194
## 122 Male 50 - 54 BAU -0.158435886
## 123 Female 55 - 59 BAU 0.004642163
## 124 Female 55 - 59 BAU -0.064129738
## 125 Female 55 - 59 BAU -0.197637574
## 126 Female 55 - 59 BAU -0.152625783
## 127 Female 55 - 59 BAU -0.153224403
## 128 Male 55 - 59 BAU 0.006693985
## 129 Male 55 - 59 BAU -0.094635480
## 130 Male 55 - 59 BAU -0.281286382
## 131 Male 55 - 59 BAU -0.219967194
## 132 Male 55 - 59 BAU -0.158435886
## 133 Female 60 - 64 BAU 0.004642163
## 134 Female 60 - 64 BAU -0.064129738
## 135 Female 60 - 64 BAU -0.197637574
## 136 Female 60 - 64 BAU -0.152625783
## 137 Female 60 - 64 BAU -0.153224403
## 138 Male 60 - 64 BAU 0.006693985
## 139 Male 60 - 64 BAU -0.094635480
## 140 Male 60 - 64 BAU -0.281286382
## 141 Male 60 - 64 BAU -0.219967194
## 142 Male 60 - 64 BAU -0.158435886
## 143 Female 65 - 69 BAU 0.004642163
## 144 Female 65 - 69 BAU -0.064129738
## 145 Female 65 - 69 BAU -0.197637574
## 146 Female 65 - 69 BAU -0.152625783
## 147 Female 65 - 69 BAU -0.153224403
## 148 Male 65 - 69 BAU 0.006693985
## 149 Male 65 - 69 BAU -0.094635480
## 150 Male 65 - 69 BAU -0.281286382
## 151 Male 65 - 69 BAU -0.219967194
## 152 Male 65 - 69 BAU -0.158435886
## 153 Female 70 - 74 BAU 0.004642163
## 154 Female 70 - 74 BAU -0.064129738
## 155 Female 70 - 74 BAU -0.197637574
## 156 Female 70 - 74 BAU -0.152625783
## 157 Female 70 - 74 BAU -0.153224403
## 158 Male 70 - 74 BAU 0.006693985
## 159 Male 70 - 74 BAU -0.094635480
## 160 Male 70 - 74 BAU -0.281286382
## 161 Male 70 - 74 BAU -0.219967194
## 162 Male 70 - 74 BAU -0.158435886
## 163 Female 75 - 79 BAU 0.004642163
## 164 Female 75 - 79 BAU -0.064129738
## 165 Female 75 - 79 BAU -0.197637574
## 166 Female 75 - 79 BAU -0.152625783
## 167 Female 75 - 79 BAU -0.153224403
## 168 Male 75 - 79 BAU 0.006693985
## 169 Male 75 - 79 BAU -0.094635480
## 170 Male 75 - 79 BAU -0.281286382
## 171 Male 75 - 79 BAU -0.219967194
## 172 Male 75 - 79 BAU -0.158435886
## 173 Female 80 - 84 BAU 0.004642163
## 174 Female 80 - 84 BAU -0.064129738
## 175 Female 80 - 84 BAU -0.197637574
## 176 Female 80 - 84 BAU -0.152625783
## 177 Female 80 - 84 BAU -0.153224403
## 178 Male 80 - 84 BAU 0.006693985
## 179 Male 80 - 84 BAU -0.094635480
## 180 Male 80 - 84 BAU -0.281286382
## 181 Male 80 - 84 BAU -0.219967194
## 182 Male 80 - 84 BAU -0.158435886
## 183 Female 85+ BAU 0.004642163
## 184 Female 85+ BAU -0.064129738
## 185 Female 85+ BAU -0.197637574
## 186 Female 85+ BAU -0.152625783
## 187 Female 85+ BAU -0.153224403
## 188 Male 85+ BAU 0.006693985
## 189 Male 85+ BAU -0.094635480
## 190 Male 85+ BAU -0.281286382
## 191 Male 85+ BAU -0.219967194
## 192 Male 85+ BAU -0.158435886
## 193 Female Undefined BAU 0.004642163
## 194 Female Undefined BAU -7.090909091
## 195 Female Undefined BAU 2.000000000
## 196 Female Undefined BAU 1.000000000
## 197 Female Undefined BAU 0.000000000
## 198 Female Undefined BAU -0.064129738
## 199 Female Undefined BAU -0.197637574
## 200 Female Undefined BAU -0.152625783
## 201 Female Undefined BAU -0.153224403
## 202 Male Undefined BAU 0.006693985
## 203 Male Undefined BAU -7.090909091
## 204 Male Undefined BAU 2.000000000
## 205 Male Undefined BAU 1.000000000
## 206 Male Undefined BAU 1.000000000
## 207 Male Undefined BAU -0.094635480
## 208 Male Undefined BAU -0.281286382
## 209 Male Undefined BAU -0.219967194
## 210 Male Undefined BAU -0.158435886oprint(summary(population,"mean"))## Gender Age mean
## 1 Female 0 - 4 125040
## 2 Male 0 - 4 130884
## 3 Female 5 - 9 149633
## 4 Male 5 - 9 156654
## 5 Female 10 - 14 151113
## 6 Male 10 - 14 157712
## 7 Female 15 - 19 144441
## 8 Male 15 - 19 152230
## 9 Female 20 - 24 152265
## 10 Male 20 - 24 161679
## 11 Female 25 - 29 172593
## 12 Male 25 - 29 183092
## 13 Female 30 - 34 169653
## 14 Male 30 - 34 181115
## 15 Female 35 - 39 174660
## 16 Male 35 - 39 186122
## 17 Female 40 - 44 168547
## 18 Male 40 - 44 177928
## 19 Female 45 - 49 154391
## 20 Male 45 - 49 159982
## 21 Female 50 - 54 176612
## 22 Male 50 - 54 179182
## 23 Female 55 - 59 185152
## 24 Male 55 - 59 183719
## 25 Female 60 - 64 183336
## 26 Male 60 - 64 176283
## 27 Female 65 - 69 185685
## 28 Male 65 - 69 171275
## 29 Female 70 - 74 186034
## 30 Male 70 - 74 163697
## 31 Female 75 - 79 118190
## 32 Male 75 - 79 93987
## 33 Female 80 - 84 96256
## 34 Male 80 - 84 65140
## 35 Female 85+ 103429
## 36 Male 85+ 47581
## 37 Female Undefined 2797030
## 38 Male Undefined 2728262oprint(summary(RR,"mean"))## Exposure_agent Response ER_function Scaling Scenario Gender
## 1 Fish All-cause mortality RR None BAU Female
## 2 Omega3 Breast cancer RR None BAU Female
## 3 Fish Depression RR None BAU Female
## 4 Omega3 CHD2 mortality Relative Hill None BAU Female
## 5 Fish All-cause mortality RR None BAU Male
## 6 Omega3 Breast cancer RR None BAU Male
## 7 Fish Depression RR None BAU Male
## 8 Omega3 CHD2 mortality Relative Hill None BAU Male
## 9 Fish All-cause mortality RR None BAU Female
## 10 Omega3 Breast cancer RR None BAU Female
## 11 Fish Depression RR None BAU Female
## 12 Omega3 CHD2 mortality Relative Hill None BAU Female
## 13 Fish All-cause mortality RR None BAU Male
## 14 Omega3 Breast cancer RR None BAU Male
## 15 Fish Depression RR None BAU Male
## 16 Omega3 CHD2 mortality Relative Hill None BAU Male
## 17 Fish All-cause mortality RR None BAU Female
## 18 Omega3 Breast cancer RR None BAU Female
## 19 Fish Depression RR None BAU Female
## 20 Omega3 CHD2 mortality Relative Hill None BAU Female
## 21 Fish All-cause mortality RR None BAU Male
## 22 Omega3 Breast cancer RR None BAU Male
## 23 Fish Depression RR None BAU Male
## 24 Omega3 CHD2 mortality Relative Hill None BAU Male
## 25 Fish All-cause mortality RR None BAU Female
## 26 Omega3 Breast cancer RR None BAU Female
## 27 Fish Depression RR None BAU Female
## 28 Omega3 CHD2 mortality Relative Hill None BAU Female
## 29 Fish All-cause mortality RR None BAU Male
## 30 Omega3 Breast cancer RR None BAU Male
## 31 Fish Depression RR None BAU Male
## 32 Omega3 CHD2 mortality Relative Hill None BAU Male
## 33 Fish All-cause mortality RR None BAU Female
## 34 Omega3 Breast cancer RR None BAU Female
## 35 Fish Depression RR None BAU Female
## 36 Omega3 CHD2 mortality Relative Hill None BAU Female
## 37 Fish All-cause mortality RR None BAU Male
## 38 Omega3 Breast cancer RR None BAU Male
## 39 Fish Depression RR None BAU Male
## 40 Omega3 CHD2 mortality Relative Hill None BAU Male
## 41 Fish All-cause mortality RR None BAU Female
## 42 Omega3 Breast cancer RR None BAU Female
## 43 Fish Depression RR None BAU Female
## 44 Omega3 CHD2 mortality Relative Hill None BAU Female
## 45 Fish All-cause mortality RR None BAU Male
## 46 Omega3 Breast cancer RR None BAU Male
## 47 Fish Depression RR None BAU Male
## 48 Omega3 CHD2 mortality Relative Hill None BAU Male
## 49 Fish All-cause mortality RR None BAU Female
## 50 Omega3 Breast cancer RR None BAU Female
## 51 Fish Depression RR None BAU Female
## 52 Omega3 CHD2 mortality Relative Hill None BAU Female
## 53 Fish All-cause mortality RR None BAU Male
## 54 Omega3 Breast cancer RR None BAU Male
## 55 Fish Depression RR None BAU Male
## 56 Omega3 CHD2 mortality Relative Hill None BAU Male
## 57 Fish All-cause mortality RR None BAU Female
## 58 Omega3 Breast cancer RR None BAU Female
## 59 Fish Depression RR None BAU Female
## 60 Omega3 CHD2 mortality Relative Hill None BAU Female
## 61 Fish All-cause mortality RR None BAU Male
## 62 Omega3 Breast cancer RR None BAU Male
## 63 Fish Depression RR None BAU Male
## 64 Omega3 CHD2 mortality Relative Hill None BAU Male
## 65 Fish All-cause mortality RR None BAU Female
## 66 Omega3 Breast cancer RR None BAU Female
## 67 Fish Depression RR None BAU Female
## 68 Omega3 CHD2 mortality Relative Hill None BAU Female
## 69 Fish All-cause mortality RR None BAU Male
## 70 Omega3 Breast cancer RR None BAU Male
## 71 Fish Depression RR None BAU Male
## 72 Omega3 CHD2 mortality Relative Hill None BAU Male
## 73 Fish All-cause mortality RR None BAU Female
## 74 Omega3 Breast cancer RR None BAU Female
## 75 Fish Depression RR None BAU Female
## 76 Omega3 CHD2 mortality Relative Hill None BAU Female
## 77 Fish All-cause mortality RR None BAU Male
## 78 Omega3 Breast cancer RR None BAU Male
## 79 Fish Depression RR None BAU Male
## 80 Omega3 CHD2 mortality Relative Hill None BAU Male
## 81 Fish All-cause mortality RR None BAU Female
## 82 Omega3 Breast cancer RR None BAU Female
## 83 Fish Depression RR None BAU Female
## 84 Omega3 CHD2 mortality Relative Hill None BAU Female
## 85 Fish All-cause mortality RR None BAU Male
## 86 Omega3 Breast cancer RR None BAU Male
## 87 Fish Depression RR None BAU Male
## 88 Omega3 CHD2 mortality Relative Hill None BAU Male
## 89 Fish All-cause mortality RR None BAU Female
## 90 Omega3 Breast cancer RR None BAU Female
## 91 Fish Depression RR None BAU Female
## 92 Omega3 CHD2 mortality Relative Hill None BAU Female
## 93 Fish All-cause mortality RR None BAU Male
## 94 Omega3 Breast cancer RR None BAU Male
## 95 Fish Depression RR None BAU Male
## 96 Omega3 CHD2 mortality Relative Hill None BAU Male
## 97 Fish All-cause mortality RR None BAU Female
## 98 Omega3 Breast cancer RR None BAU Female
## 99 Fish Depression RR None BAU Female
## 100 Omega3 CHD2 mortality Relative Hill None BAU Female
## 101 Fish All-cause mortality RR None BAU Male
## 102 Omega3 Breast cancer RR None BAU Male
## 103 Fish Depression RR None BAU Male
## 104 Omega3 CHD2 mortality Relative Hill None BAU Male
## 105 Fish All-cause mortality RR None BAU Female
## 106 Omega3 Breast cancer RR None BAU Female
## 107 Fish Depression RR None BAU Female
## 108 Omega3 CHD2 mortality Relative Hill None BAU Female
## 109 Fish All-cause mortality RR None BAU Male
## 110 Omega3 Breast cancer RR None BAU Male
## 111 Fish Depression RR None BAU Male
## 112 Omega3 CHD2 mortality Relative Hill None BAU Male
## 113 Fish All-cause mortality RR None BAU Female
## 114 Omega3 Breast cancer RR None BAU Female
## 115 Fish Depression RR None BAU Female
## 116 Omega3 CHD2 mortality Relative Hill None BAU Female
## 117 Fish All-cause mortality RR None BAU Male
## 118 Omega3 Breast cancer RR None BAU Male
## 119 Fish Depression RR None BAU Male
## 120 Omega3 CHD2 mortality Relative Hill None BAU Male
## 121 Fish All-cause mortality RR None BAU Female
## 122 Omega3 Breast cancer RR None BAU Female
## 123 Fish Depression RR None BAU Female
## 124 Omega3 CHD2 mortality Relative Hill None BAU Female
## 125 Fish All-cause mortality RR None BAU Male
## 126 Omega3 Breast cancer RR None BAU Male
## 127 Fish Depression RR None BAU Male
## 128 Omega3 CHD2 mortality Relative Hill None BAU Male
## 129 Fish All-cause mortality RR None BAU Female
## 130 Omega3 Breast cancer RR None BAU Female
## 131 Fish Depression RR None BAU Female
## 132 Omega3 CHD2 mortality Relative Hill None BAU Female
## 133 Fish All-cause mortality RR None BAU Male
## 134 Omega3 Breast cancer RR None BAU Male
## 135 Fish Depression RR None BAU Male
## 136 Omega3 CHD2 mortality Relative Hill None BAU Male
## 137 Fish All-cause mortality RR None BAU Female
## 138 Omega3 Breast cancer RR None BAU Female
## 139 Fish Depression RR None BAU Female
## 140 Omega3 CHD2 mortality Relative Hill None BAU Female
## 141 Fish All-cause mortality RR None BAU Male
## 142 Omega3 Breast cancer RR None BAU Male
## 143 Fish Depression RR None BAU Male
## 144 Omega3 CHD2 mortality Relative Hill None BAU Male
## 145 Fish All-cause mortality RR None BAU Female
## 146 Omega3 Breast cancer RR None BAU Female
## 147 Fish Depression RR None BAU Female
## 148 Omega3 CHD2 mortality Relative Hill None BAU Female
## 149 Fish All-cause mortality RR None BAU Male
## 150 Omega3 Breast cancer RR None BAU Male
## 151 Fish Depression RR None BAU Male
## 152 Omega3 CHD2 mortality Relative Hill None BAU Male
## Exposure Age mean
## 1 Direct 0 - 4 0.9358703
## 2 Direct 0 - 4 0.8023624
## 3 Direct 0 - 4 0.8473742
## 4 Direct 0 - 4 0.8467756
## 5 Direct 0 - 4 0.9053645
## 6 Direct 0 - 4 0.7187136
## 7 Direct 0 - 4 0.7800328
## 8 Direct 0 - 4 0.8415641
## 9 Direct 5 - 9 0.9358703
## 10 Direct 5 - 9 0.8023624
## 11 Direct 5 - 9 0.8473742
## 12 Direct 5 - 9 0.8467756
## 13 Direct 5 - 9 0.9053645
## 14 Direct 5 - 9 0.7187136
## 15 Direct 5 - 9 0.7800328
## 16 Direct 5 - 9 0.8415641
## 17 Direct 10 - 14 0.9358703
## 18 Direct 10 - 14 0.8023624
## 19 Direct 10 - 14 0.8473742
## 20 Direct 10 - 14 0.8467756
## 21 Direct 10 - 14 0.9053645
## 22 Direct 10 - 14 0.7187136
## 23 Direct 10 - 14 0.7800328
## 24 Direct 10 - 14 0.8415641
## 25 Direct 15 - 19 0.9358703
## 26 Direct 15 - 19 0.8023624
## 27 Direct 15 - 19 0.8473742
## 28 Direct 15 - 19 0.8467756
## 29 Direct 15 - 19 0.9053645
## 30 Direct 15 - 19 0.7187136
## 31 Direct 15 - 19 0.7800328
## 32 Direct 15 - 19 0.8415641
## 33 Direct 20 - 24 0.9358703
## 34 Direct 20 - 24 0.8023624
## 35 Direct 20 - 24 0.8473742
## 36 Direct 20 - 24 0.8467756
## 37 Direct 20 - 24 0.9053645
## 38 Direct 20 - 24 0.7187136
## 39 Direct 20 - 24 0.7800328
## 40 Direct 20 - 24 0.8415641
## 41 Direct 25 - 29 0.9358703
## 42 Direct 25 - 29 0.8023624
## 43 Direct 25 - 29 0.8473742
## 44 Direct 25 - 29 0.8467756
## 45 Direct 25 - 29 0.9053645
## 46 Direct 25 - 29 0.7187136
## 47 Direct 25 - 29 0.7800328
## 48 Direct 25 - 29 0.8415641
## 49 Direct 30 - 34 0.9358703
## 50 Direct 30 - 34 0.8023624
## 51 Direct 30 - 34 0.8473742
## 52 Direct 30 - 34 0.8467756
## 53 Direct 30 - 34 0.9053645
## 54 Direct 30 - 34 0.7187136
## 55 Direct 30 - 34 0.7800328
## 56 Direct 30 - 34 0.8415641
## 57 Direct 35 - 39 0.9358703
## 58 Direct 35 - 39 0.8023624
## 59 Direct 35 - 39 0.8473742
## 60 Direct 35 - 39 0.8467756
## 61 Direct 35 - 39 0.9053645
## 62 Direct 35 - 39 0.7187136
## 63 Direct 35 - 39 0.7800328
## 64 Direct 35 - 39 0.8415641
## 65 Direct 40 - 44 0.9358703
## 66 Direct 40 - 44 0.8023624
## 67 Direct 40 - 44 0.8473742
## 68 Direct 40 - 44 0.8467756
## 69 Direct 40 - 44 0.9053645
## 70 Direct 40 - 44 0.7187136
## 71 Direct 40 - 44 0.7800328
## 72 Direct 40 - 44 0.8415641
## 73 Direct 45 - 49 0.9358703
## 74 Direct 45 - 49 0.8023624
## 75 Direct 45 - 49 0.8473742
## 76 Direct 45 - 49 0.8467756
## 77 Direct 45 - 49 0.9053645
## 78 Direct 45 - 49 0.7187136
## 79 Direct 45 - 49 0.7800328
## 80 Direct 45 - 49 0.8415641
## 81 Direct 50 - 54 0.9358703
## 82 Direct 50 - 54 0.8023624
## 83 Direct 50 - 54 0.8473742
## 84 Direct 50 - 54 0.8467756
## 85 Direct 50 - 54 0.9053645
## 86 Direct 50 - 54 0.7187136
## 87 Direct 50 - 54 0.7800328
## 88 Direct 50 - 54 0.8415641
## 89 Direct 55 - 59 0.9358703
## 90 Direct 55 - 59 0.8023624
## 91 Direct 55 - 59 0.8473742
## 92 Direct 55 - 59 0.8467756
## 93 Direct 55 - 59 0.9053645
## 94 Direct 55 - 59 0.7187136
## 95 Direct 55 - 59 0.7800328
## 96 Direct 55 - 59 0.8415641
## 97 Direct 60 - 64 0.9358703
## 98 Direct 60 - 64 0.8023624
## 99 Direct 60 - 64 0.8473742
## 100 Direct 60 - 64 0.8467756
## 101 Direct 60 - 64 0.9053645
## 102 Direct 60 - 64 0.7187136
## 103 Direct 60 - 64 0.7800328
## 104 Direct 60 - 64 0.8415641
## 105 Direct 65 - 69 0.9358703
## 106 Direct 65 - 69 0.8023624
## 107 Direct 65 - 69 0.8473742
## 108 Direct 65 - 69 0.8467756
## 109 Direct 65 - 69 0.9053645
## 110 Direct 65 - 69 0.7187136
## 111 Direct 65 - 69 0.7800328
## 112 Direct 65 - 69 0.8415641
## 113 Direct 70 - 74 0.9358703
## 114 Direct 70 - 74 0.8023624
## 115 Direct 70 - 74 0.8473742
## 116 Direct 70 - 74 0.8467756
## 117 Direct 70 - 74 0.9053645
## 118 Direct 70 - 74 0.7187136
## 119 Direct 70 - 74 0.7800328
## 120 Direct 70 - 74 0.8415641
## 121 Direct 75 - 79 0.9358703
## 122 Direct 75 - 79 0.8023624
## 123 Direct 75 - 79 0.8473742
## 124 Direct 75 - 79 0.8467756
## 125 Direct 75 - 79 0.9053645
## 126 Direct 75 - 79 0.7187136
## 127 Direct 75 - 79 0.7800328
## 128 Direct 75 - 79 0.8415641
## 129 Direct 80 - 84 0.9358703
## 130 Direct 80 - 84 0.8023624
## 131 Direct 80 - 84 0.8473742
## 132 Direct 80 - 84 0.8467756
## 133 Direct 80 - 84 0.9053645
## 134 Direct 80 - 84 0.7187136
## 135 Direct 80 - 84 0.7800328
## 136 Direct 80 - 84 0.8415641
## 137 Direct 85+ 0.9358703
## 138 Direct 85+ 0.8023624
## 139 Direct 85+ 0.8473742
## 140 Direct 85+ 0.8467756
## 141 Direct 85+ 0.9053645
## 142 Direct 85+ 0.7187136
## 143 Direct 85+ 0.7800328
## 144 Direct 85+ 0.8415641
## 145 Direct Undefined 0.9358703
## 146 Direct Undefined 0.8023624
## 147 Direct Undefined 0.8473742
## 148 Direct Undefined 0.8467756
## 149 Direct Undefined 0.9053645
## 150 Direct Undefined 0.7187136
## 151 Direct Undefined 0.7800328
## 152 Direct Undefined 0.8415641###################
# Graphs
trim <- function(ova) return(oapply(ova, NULL, mean, "Iter")@output)
ggplot(amount@output, aes(x=Gender, weight=amountResult, fill=Kala))+geom_bar()+
labs(
title="Kalansyönti Suomessa ikäryhmittäin",
y="Syönti (g/d)"
)
ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")## Saving 7 x 5 in imageplot_ly(trim(total_amount), x=~Kala, y=~total_amountResult, color=~Kala, type="bar") %>%
layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.plot_ly(trim(conc_vit), x=~Nutrient, y=~conc_vitResult, color=~Kala, type="scatter", mode="markers") %>%
layout(yaxis=list(title="Concentrations of nutrients (mg or ug /g)"))## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsggplot(conc_pfas@output, aes(x=Fish, y=conc_pfasResult))+geom_point()+ # Inputed data for missing species.
coord_flip()
ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
scale_x_log10()+
stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
scale_linetype_manual(values=c("dotted","solid","twodash"))+
labs(
title="PFAS concentration in fishes in Finland",
x="PFAS concentration (ng/g fresh weight)",
y="Cumulative probability"
)
# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")## Saving 7 x 5 in imageggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
facet_wrap(~Compound, scales="free_x")
#ggplot(oapply(expo_dir, cols=c("Iter"),FUN=mean)@output,
# aes(x=Gender, weight=expo_dirResult,fill=Fish))+geom_bar()+
# facet_wrap(~Compound, scales="free_y")+
# labs(title="Eri yhdisteiden saanti kalasta")
#ggsave("Yhdisteiden saanti kalasta Suomessa.svg")
plot_ly(trim(exposure), x=~Gender, y=~exposureResult, color=~Exposure_agent, text=~Exposure_agent, type="bar") %>%
layout(yaxis=list(title="Exposure to nutrients (g or ug /d)"))ggplot(trim(exposure), aes(x=Gender, weight=exposureResult, fill=Gender))+geom_bar()+
facet_wrap(~Exposure_agent, scales="free_y")+
labs(
title="Exposure to compounds",
y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
)
cat("Kalaperäisiä tautitaakkoja Suomessa\n")## Kalaperäisiä tautitaakkoja Suomessaif(openv$N>1) {
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
tmp <- data.frame(
Altiste = tmp$Exposure_agent,
Vaikutus = tmp$Response,
Keskiarvo = as.character(signif(tmp$mean,2)),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)#[rev(match(lev, tmp$Exposure_agent)),]
oprint(tmp)
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
tmp <- data.frame(
Terveysvaikutus = tmp$Response,
Keskiarvo = signif(tmp$mean,2),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)
oprint(tmp)
}
ggplot(trim(BoDattr), aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()
ggplot(trim(BoDattr), aes(x=Response, weight=BoDattrResult, fill=Exposure_agent))+geom_bar()
ggplot(trim(BoDattr), aes(x=Age, weight=BoDattrResult, fill=Response))+geom_bar(position="stack")
plot_ly(trim(BoDattr), x=~Exposure_agent, y=~BoDattrResult, color=~Response, text=~paste(Age, Exposure_agent, sep=": "), type="bar") %>%
layout(yaxis=list(title="Disease burden (DALY /a); CHD2=coronary heart disease"), barmode="stack")## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors################ Insight network
gr <- scrape(type="assessment")
objects.latest("Op_en3861", "makeGraph") # [[Insight network]]## Loading objects:
## makeGraphgr <- makeGraph(gr)## Loading required package: DiagrammeR## Loading objects:
## formatted
## Loading objects:
## chooseGr#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
objects.latest("Op_en6007", code_name="diagnostics")## Loading objects:
## showind
## binoptest
## showLoctable
## ovashapetestshowLoctable()tmp <- showind()## Hg is not an ovariable.
## subgrouping is not an ovariable.
## mc2d is not an ovariable.
## sumExposcen is not an ovariable.
## mc2d is not an ovariable.
## mc2dparam is not an ovariable.
## TEFversion is not an ovariable.